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Instructor: Preethi Jyothi Aug 21, 2017


Automatic Speech Recognition (CS753)

Lecture 9: RNN-based architectures for ASR

Automatic Speech Recognition (CS753)

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Recap: Hybrid DNN-HMM Systems

Instead of GMMs, use scaled DNN posteriors as the HMM observation probabilities

DNN trained using triphone labels derived from a forced alignment “Viterbi” step.

Forced alignment: Given a training utterance {O,W}, find the most

likely sequence of states (and

hence triphone state labels) using a set of trained triphone HMM models, M. Here M is constrained by the triphones in W.

DAHL et al.: CONTEXT-DEPENDENT PRE-TRAINED DEEP NEURAL NETWORKS FOR LVSR 35

Fig. 1. Diagram of our hybrid architecture employing a deep neural network.

The HMM models the sequential property of the speech signal, and the DNN models the scaled observation likelihood of all the senones (tied tri-phone states). The same DNN is replicated over different points in time.

A. Architecture of CD-DNN-HMMs

Fig. 1 illustrates the architecture of our proposed CD-DNN- HMMs. The foundation of the hybrid approach is the use of a forced alignment to obtain a frame level labeling for training the ANN. The key difference between the CD-DNN-HMM archi- tecture and earlier ANN-HMM hybrid architectures (and con- text-independent DNN-HMMs) is that we model senones as the DNN output units directly. The idea of using senones as the modeling unit has been proposed in [22] where the posterior probabilities of senones were estimated using deep-structured conditional random fields (CRFs) and only one audio frame was used as the input of the posterior probability estimator.

This change offers two primary advantages. First, we can im- plement a CD-DNN-HMM system with only minimal modifica- tions to an existing CD-GMM-HMM system, as we will show in Section II-B. Second, any improvements in modeling units that are incorporated into the CD-GMM-HMM baseline system, such as cross-word triphone models, will be accessible to the DNN through the use of the shared training labels.

If DNNs can be trained to better predict senones, then CD-DNN-HMMs can achieve better recognition accu- racy than tri-phone GMM-HMMs. More precisely, in our CD-DNN-HMMs, the decoded word sequence is determined as

(13) where is the language model (LM) probability, and

(14)

(15) is the acoustic model (AM) probability. Note that the observa- tion probability is

(16)

where is the state (senone) posterior probability esti- mated from the DNN, is the prior probability of each state (senone) estimated from the training set, and is indepen- dent of the word sequence and thus can be ignored. Although dividing by the prior probability (called scaled likelihood estimation by [38], [40], [41]) may not give improved recog- nition accuracy under some conditions, we have found it to be very important in alleviating the label bias problem, especially when the training utterances contain long silence segments.

B. Training Procedure of CD-DNN-HMMs

CD-DNN-HMMs can be trained using the embedded Viterbi algorithm. The main steps involved are summarized in Algo- rithm 1, which takes advantage of the triphone tying structures and the HMMs of the CD-GMM-HMM system. Note that the logical triphone HMMs that are effectively equivalent are clus- tered and represented by a physical triphone (i.e., several log- ical triphones are mapped to the same physical triphone). Each physical triphone has several (typically 3) states which are tied and represented by senones. Each senone is given a

as the label to fine-tune the DNN. The mapping maps each physical triphone state to the corresponding . Algorithmic 1 Main Steps to Train CD-DNN-HMMs

1) Train a best tied-state CD-GMM-HMM system where state tying is determined based on the data-driven

decision tree. Denote the CD-GMM-HMM gmm-hmm.

2) Parse gmm-hmm and give each senone name an

ordered starting from 0. The will

be served as the training label for DNN fine-tuning.

3) Parse gmm-hmm and generate a mapping from each physical tri-phone state (e.g., b-ah t.s2) to the corresponding . Denote this mapping

.

4) Convert gmm-hmm to the corresponding

CD-DNN-HMM by borrowing the

tri-phone and senone structure as well as the transition probabilities from .

5) Pre-train each layer in the DNN bottom-up layer by layer and call the result ptdnn.

6) Use to generate a state-level alignment on the training set. Denote the alignment .

7) Convert to where each physical tri-phone state is converted to .

8) Use the associated with each frame in

to fine-tune the DBN using back-propagation or other approaches, starting from . Denote the DBN

.

9) Estimate the prior probability , where is the number of frames associated with senone in and is the total number of frames.

10) Re-estimate the transition probabilities using and to maximize the likelihood of observing the features. Denote the new CD-DNN-HMM

.

11) Exit if no recognition accuracy improvement is observed in the development set; Otherwise use

Fixed window of 
 5 speech frames

Triphone state labels
 (DNN posteriors)

39 features in one frame

… …

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Recap: Tandem DNN-HMM Systems

Neural network outputs are used as “features” to train HMM-GMM models

Use a low-dimensional

bottleneck layer representation to extract features from the

bottleneck layer


Bottleneck Layer Output Layer

Input Layer

(4)

Feedforward DNNs we’ve seen so far…

Assume independence among the training instances

Independent decision made about classifying each individual speech frame

Network state is completely reset after each speech 
 frame is processed

This independence assumption fails for data like speech which has temporal and sequential structure

(5)

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) work naturally with sequential data and process it one element at a time

HMMs also similarly attempt to model time dependencies.

How’s it different?

HMMs are limited by the size of the state space. Inference becomes intractable if the state space grows very large!

What about RNNs?

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RNN definition

Two main equations govern RNNs:

H, O

x

t

y

t

h

t

unfold

H, O

x

1

y

1

h

0

H, O

x

2

y

2

h

1

H, O

x

3

y

3

h

2

ht = H(Wxt + Vht-1 + b(h)) yt = O(Uht + b(y))

where W, V, U are matrices of input-hidden weights, hidden-hidden
 weights and hidden-output weights resp; b(h) and b(y) are bias vectors

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Recurrent Neural Networks

Recurrent Neural Networks (RNNs) work naturally with sequential data and process it one element at a time

HMMs also similarly attempt to model time dependencies.

How’s it different?

HMMs are limited by the size of the state space. Inference becomes intractable if the state space grows very large!

What about RNNs? RNNs are designed to capture long- range dependencies unlike HMMs: Network state is

exponential in the number of nodes in a hidden layer

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Training RNNs

An unrolled RNN is just a very deep feedforward network

For a given input sequence:

create the unrolled network

add a loss function node to the network

then, use backpropagation to compute the gradients

(9)

Backpropagation

u

L v

∂L/∂u = Σv ∈ Γ(u) ∂L/∂v ∂v/∂u

Backpropagation Base case: ∂L/∂L = 1 For each u (top to bottom):

For each v Γ(u):

Inductively, have
 computed ∂L/∂v

Directly compute ∂v/∂u Compute ∂L/∂u

Forward Pass

First, in a forward pass, compute values of all

nodes given an input


(The values of each node will be needed during

backprop)

Compute ∂L/∂w 


where ∂L/∂w = ∂L/∂u ⋅ ∂u/∂w Where values computed in the forward pass may be needed

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Training RNNs

An unrolled RNN is just a very deep feedforward network

For a given input sequence:

create the unrolled network

add a loss function node to the network

then, use backpropagation to compute the gradients

This algorithm is known as backpropagation through time (BPTT)


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Deep RNNs

RNNs can be stacked in layers to form deep RNNs

Empirically shown to perform better than shallow RNNs on ASR [G13]

H, O

x

1

y

1

h

0,1 H, O

x

2

y

2

h

1,1 H, O

x

3

y

3

h

2,1

H, O H, O H, O

h

0,2

h

1,2

h

2,2

[G13] A. Graves, A . Mohamed, G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks”, ICASSP, 2013.

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Vanilla RNN Model

ht = H(Wxt + Vht-1 + b(h)) yt = O(Uht + b(y))

H : element wise application of the sigmoid or tanh function O : the softmax function

Run into problems of exploding and vanishing gradients.

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Exploding/Vanishing Gradients

To address this problem in RNNs, Long Short Term Memory (LSTM) units were proposed [HS97]

[HS97] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” 


Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

In deep networks, gradients in early layers is computed as the product of terms from all the later layers

This leads to unstable gradients:

If the terms in later layers are large enough, gradients in early layers (which is the product of these terms) can grow

exponentially large: Exploding gradients

If the terms are in later layers are small, gradients in early

layers will tend to exponentially decrease: Vanishing gradients

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Long Short Term Memory Cells

Memory cell: Neuron that stores information over long time periods

Forget gate: When on, memory cell retains previous contents.

Otherwise, memory cell forgets contents.

When input gate is on, write into memory cell

When output gate is on, read from the memory cell

Input Gate

Output Gate Memory

Cell

Forget Gate

⊗ ⊗

(15)

Bidirectional RNNs

BiRNNs process the data in both directions with two separate 
 hidden layers

Outputs from both hidden layers are concatenated at each position

Hf, Of

x

hello

h

0,f Hf, Of

x

world

h

1,f Hf, Of

x

.

h

2,f

Hb, Ob

h

3,b Hb, Ob

h

2,b Hb, Ob

h

1,b

concat concat concat

y

1,f

y

3,b

y

2,f

y

2,b

y

3,f

y

1,b

h

3,f

h

0,b

Forward
 layer Backward


layer

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Automatic Speech Recognition (CS753)

RNN-based ASR system

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ASR with RNNs

We have seen how neural networks can be used for acoustic models in ASR systems

Main limitation: Frame-level training targets derived from HMM- based alignments

Goal: Single RNN model that addresses these issues and replaces as much of the speech pipeline as possible [G14]

[G14] A. Graves, N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks”, ICML, 2014.

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RNN Architecture

H was implemented using LSTMs in [G14]. Input: Acoustic feature vectors, one per frame; Output: Characters + space

Deep bidirectional LSTM networks were used Hf, Of

x

t-1

h

0,f Hf, Of

x

t

h

1,f Hf, Of

x

t+1

h

2,f

Hb, Ob

h

3,b Hb, Ob

h

2,b Hb, Ob

h

1,b

h

3,f

h

0,b

y

t-1

y

t

y

t+1

[G14] A. Graves, N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks”, ICML, 2014.

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Connectionist Temporal Classification (CTC)

Neural networks in ASR are trained at the frame-level and typically require alignments between the acoustics and the word sequence during training telling you which label (e.g.

triphone state) should be output at each timestep

CTC tries to get around this!

This is an objective function that allows RNN training without this explicit alignment step: CTC considers all possible

alignments

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CTC: Pre-requisites

Augment the output vocabulary with an additional “blank” (_) label

For a given label sequence, there can be multiple alignments:

(x, y, z) could correspond to (x, _, y, _, _, z) or (_, x, x, _, y, z)

Define a 2-step operator B that reduces a label sequence by first, removing repeating labels and second, removing blanks.

B(“x, _, y, _, _, z”) = B(“_, x, x, _, y, z”) = “x, y, z”

(21)

CTC Objective Function

CTC objective function is the probability of an output label sequence y given an utterance x

Here, we sum over all possible alignments for y, enumerated by B-1(y)

CTC assumes that Pr(a|x) can be computed as

i.e. CTC assumes that outputs at each time-step are conditionally independent given the input

Efficient dynamic programming algorithm to compute this loss function and its gradients [GJ14]

YT

t=1

Pr(at|x)

CTC(x, y) = Pr(y|x) = X

a2B 1(y)

Pr(a|x)

[GJ14] Towards End-to-End Speech Recognition with Recurrent Neural Networks, ICML 14

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Decoding

First approximation: For a given test input sequence x, pick the most probable output at each time step

More accurate decoding uses a search algorithm that also makes use of a dictionary and a language model. (Decoding search algorithms will be discussed in detail in later lectures.)

arg max

y

Pr(y|x) B(arg max

a

Pr(a|x))

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WER results

System LM WER

RNN-CTC Dictionary only 24.0

RNN-CTC Bigram 10.4

RNN-CTC Trigram 8.7

Baseline Bigram 9.4

Baseline Trigram 7.8

[G14] A. Graves, N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks”, ICML, 2014.

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Some erroneous examples produced by the end-to-end RNN

Target: “There’s unrest but we’re not going to lose them to Dukakis”

Output: “There’s unrest but we’re not going to lose them to Dekakis”

Target: “T. W. A. also plans to hang its boutique shingle in airports at Lambert Saint”

Output: “T. W. A. also plans tohing its bootik single in airports at Lambert Saint”

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Another end-to-end system

Decoding is still at the word level. Out-of-vocabulary (OOV) words cannot be handled.

Build a system that is trained and decoded entirely at the character-level.

This would enable the transcription of OOV words, disfluencies, etc.

[M et al.]: Shows results on the Switchboard task. Matches a GMM-HMM baseline system but underperforms compared to an HMM-DNN baseline.

[M et al.]:Maas et al., “Lexicon Free Conversational Speech Recognition with Neural Networks”, NAACL 15

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Model Specifics

character probabilities. The CTC collapsing func- tion achieves this by introducing a special blank symbol, which we denote using “ ”, and collapsing any repeating characters in the original length T out- put. This output symbol contains the notion of junk or other so as to not produce a character in the fi- nal output hypothesis. Our transcripts W come from some set of symbols 0 but we reason over = 0[ . We denote the collapsing function by (·) which takes an input string and produces the unique col- lapsed version of that string. As an example, here are the set of strings Z of length T = 3 such that

(z) = hi, 8z 2 Z:

Z = {hhi, hii, hi, h i, hi }.

There are a large number of possible length T sequences corresponding to a final length tran- script hypothesis. The CTC objective function LCTC(X, W) is a likelihood of the correct final tran- script W which requires integrating over the prob- abilities of all length T character sequences CW = {C : (C) = W} consistent with W after applying the collapsing function,

LCTC(X, W) = X

CW

p(C|X)

= X

CW

YT

t=1

p(ct|X).

(2)

Using a dynamic programming approach we can ex- actly compute this loss function efficiently as well as its gradient with respect to our probabilities p(ct|X).

2.2 Deep Bi-Directional Recurrent Neural Networks

Our loss function requires at each time t a probabil- ity distribution p(c|xt) over characters c given in- put features xt. We model this distribution using a DBRNN because it provides an expressive model which explicitly accounts for the sequential relation- ships that should exist in our task. Moreover, the DBRNN is a relatively straightforward neural net- work architecture to specify, and allows us to learn parameters from data rather than more explicitly specifying how to convert audio features into char- acters. Figure 1 shows a DBRNN with two hidden layers.

W(1) W(1) W(1) W(2) W(2) W(2)

W(f) W(f) W(b) W(b)

W(s) W(s) W(s)

+ + +

x h(1) h(f) h(b) p(c|x)

t 1 t t + 1

Figure 1: Deep bi-directional recurrent neural net- work to map input audio features X to a distribu- tion p(c|xt) over output characters at each timestep t. The network contains two hidden layers with the second layer having bi-directional temporal recur- rence.

A DBRNN computes the distribution p(c|xt) us- ing a series of hidden layers followed by an output layer. Given an input vector xt the first hidden layer activations are a vector computed as,

h(1) = (W(1)T xt + b(1)), (3) where the matrix W(1) and vector b(1) are the weight matrix and bias vector. The function (·) is a point-wise nonlinearity. We use (z) = min(max(z, 0), µ). This is a rectified linear acti- vation function clipped to a maximum possible ac- tivation of µ to prevent overflow. Rectified linear hidden units have been show to work well in gen- eral for deep neural networks, as well as for acoustic modeling of speech data (Glorot et al., 2011; Zeiler et al., 2013; Dahl et al., 2013; Maas et al., 2013)

We select a single hidden layer j of the network to have temporal connections. Our temporal hidden layer representation h(j) is the sum of two partial hidden layer representations,

h(j)t = h(ft ) + h(b)t . (4) The representation h(f) uses a weight matrix W(f) to propagate information forwards in time. Sim- ilarly, the representation h(b) propagates informa- tion backwards in time using a weight matrix W(b). These partial hidden representations both take input from the previous hidden layer h(j 1) using a weight

Approach consists of two neural models:

A deep bidirectional RNN (DBRNN) mapping acoustic features to character sequences (Trained using CTC.)

A neural network character language model

Image from Maas et al., “Lexicon Free Conversational Speech Recognition with Neural Networks”, NAACL 15

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Decoding

Simplest form: Decode without any language model

Beam Search decoding:

Combine DBRNN outputs with a char-level language model

Char-level language model applied at every time step (unlike word models)

Circumvents the issue of handling OOV words during decoding

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Experimental Results

Method CER EV CH SWBD

HMM-GMM 23.0 29.0 36.1 21.7 HMM-DNN 17.6 21.2 27.1 15.1

HMM-SHF NR NR NR 12.4

CTC no LM 27.7 47.1 56.1 38.0 CTC+5-gram 25.7 39.0 47.0 30.8 CTC+7-gram 24.7 35.9 43.8 27.8 CTC+NN-1 24.5 32.3 41.1 23.4 CTC+NN-3 24.0 30.9 39.9 21.8 CTC+RNN 24.9 33.0 41.7 24.2 CTC+RNN-3 24.7 30.8 40.2 21.4 Table 1: Character error rate (CER) and word er- ror rate results on the Eval2000 test set. We re- port word error rates on the full test set (EV) which consists of the Switchboard (SWBD) and CallHome (CH) subsets. As baseline systems we use an HMM- GMM system and HMM-DNN system. We evaluate our DBRNN trained using CTC by decoding with several character-level language models: 5-gram, 7- gram, densely connected neural networks with 1 and 3 hidden layers (NN-1, and NN-3), as well as recur- rent neural networks s with 1 and 3 hidden layers.

We additionally include results from a state-of-the- art HMM-based system (HMM-DNN-SHF) which does not report performance on all metrics we eval- uate (NR).

First, we build an HMM-GMM system using the Kaldi open-source toolkit2 (Povey et al., 2011). The baseline recognizer has 8,986 sub-phone states and 200K Gaussians trained using maximum likelihood.

Input features are speaker-adapted MFCCs. Overall, the baseline GMM system setup largely follows the existing s5b Kaldi recipe, and we defer to previous work for details (Vesely et al., 2013).

We additionally built an HMM-DNN system by training a DNN acoustic model using maxi- mum likelihood on the alignments produced by our HMM-GMM system. The DNN consists of five hid- den layers, each with 2,048 hidden units, for a total of approximately 36 million (M) free parameters in the acoustic model.

Both baseline systems use a bigram language

2http://kaldi.sf.net

model built from the 3M words in the Switch- board transcripts interpolated with a second bi- gram language model built from 11M words on the Fisher English Part 1 transcripts (LDC2004T19).

Both LMs are trained using interpolated Kneser- Ney smoothing. For context we also include WER results from a state-of-the-art HMM-DNN system built with quinphone phonetic context and Hessian- free sequence-discriminative training (Sainath et al., 2014).

4.2 DBRNN Training

We train a DBRNN using the CTC loss function on the entire 300hr training corpus. The input features to the DBRNN at each timestep are MFCCs with context window of ±10 frames. The DBRNN has 5 hidden layers with the third containing recurrent connections. All layers have 1824 hidden units, giv- ing about 20M trainable parameters. In preliminary experiments we found that choosing the middle hid- den layer to have recurrent connections led to the best results.

The output symbol set consists of 33 characters including the special blank character. Note that be- cause speech recognition transcriptions do not con- tain proper casing or punctuation, we exclude capi- tal letters and punctuation marks with the exception of “-”, which denotes a partial word fragment, and

“’”, as used in contractions such as “can’t.”

We train the DBRNN from random initial pa- rameters using the gradient-based Nesterov’s accel- erated gradient (NAG) algorithm as this technique is sometimes beneficial as compared with standard stochastic gradient descent for deep recurrent neural network training (Sutskever et al., 2013). The NAG algorithm uses a step size of 10 5 and a momentum of 0.95. After each epoch we divide the learning rate by 1.3. Training for 10 epochs on a single GTX 570 GPU takes approximately one week.

4.3 Character Language Model Training

The Switchboard corpus transcripts alone are too small to build CLMs which accurately model gen- eral orthography in English. To learn how to spell words more generally we train our CLMs using a corpus of 31 billion words gathered from the web (Heafield et al., 2013). Our language models use sentence start and end tokens, <s> and </s>, as

Method CER EV CH SWBD

HMM-GMM 23.0 29.0 36.1 21.7 HMM-DNN 17.6 21.2 27.1 15.1

HMM-SHF NR NR NR 12.4

CTC no LM 27.7 47.1 56.1 38.0 CTC+5-gram 25.7 39.0 47.0 30.8 CTC+7-gram 24.7 35.9 43.8 27.8 CTC+NN-1 24.5 32.3 41.1 23.4 CTC+NN-3 24.0 30.9 39.9 21.8 CTC+RNN 24.9 33.0 41.7 24.2 CTC+RNN-3 24.7 30.8 40.2 21.4 Table 1: Character error rate (CER) and word er- ror rate results on the Eval2000 test set. We re- port word error rates on the full test set (EV) which consists of the Switchboard (SWBD) and CallHome (CH) subsets. As baseline systems we use an HMM- GMM system and HMM-DNN system. We evaluate our DBRNN trained using CTC by decoding with several character-level language models: 5-gram, 7- gram, densely connected neural networks with 1 and 3 hidden layers (NN-1, and NN-3), as well as recur- rent neural networks s with 1 and 3 hidden layers.

We additionally include results from a state-of-the- art HMM-based system (HMM-DNN-SHF) which does not report performance on all metrics we eval- uate (NR).

First, we build an HMM-GMM system using the Kaldi open-source toolkit2 (Povey et al., 2011). The baseline recognizer has 8,986 sub-phone states and 200K Gaussians trained using maximum likelihood.

Input features are speaker-adapted MFCCs. Overall, the baseline GMM system setup largely follows the existing s5b Kaldi recipe, and we defer to previous work for details (Vesely et al., 2013).

We additionally built an HMM-DNN system by training a DNN acoustic model using maxi- mum likelihood on the alignments produced by our HMM-GMM system. The DNN consists of five hid- den layers, each with 2,048 hidden units, for a total of approximately 36 million (M) free parameters in the acoustic model.

Both baseline systems use a bigram language

2http://kaldi.sf.net

model built from the 3M words in the Switch- board transcripts interpolated with a second bi- gram language model built from 11M words on the Fisher English Part 1 transcripts (LDC2004T19).

Both LMs are trained using interpolated Kneser- Ney smoothing. For context we also include WER results from a state-of-the-art HMM-DNN system built with quinphone phonetic context and Hessian- free sequence-discriminative training (Sainath et al., 2014).

4.2 DBRNN Training

We train a DBRNN using the CTC loss function on the entire 300hr training corpus. The input features to the DBRNN at each timestep are MFCCs with context window of ±10 frames. The DBRNN has 5 hidden layers with the third containing recurrent connections. All layers have 1824 hidden units, giv- ing about 20M trainable parameters. In preliminary experiments we found that choosing the middle hid- den layer to have recurrent connections led to the best results.

The output symbol set consists of 33 characters including the special blank character. Note that be- cause speech recognition transcriptions do not con- tain proper casing or punctuation, we exclude capi- tal letters and punctuation marks with the exception of “-”, which denotes a partial word fragment, and

“’”, as used in contractions such as “can’t.”

We train the DBRNN from random initial pa- rameters using the gradient-based Nesterov’s accel- erated gradient (NAG) algorithm as this technique is sometimes beneficial as compared with standard stochastic gradient descent for deep recurrent neural network training (Sutskever et al., 2013). The NAG algorithm uses a step size of 10 5 and a momentum of 0.95. After each epoch we divide the learning rate by 1.3. Training for 10 epochs on a single GTX 570 GPU takes approximately one week.

4.3 Character Language Model Training

The Switchboard corpus transcripts alone are too small to build CLMs which accurately model gen- eral orthography in English. To learn how to spell words more generally we train our CLMs using a corpus of 31 billion words gathered from the web (Heafield et al., 2013). Our language models use sentence start and end tokens, <s> and </s>, as Image from Maas et al., “Lexicon Free Conversational Speech Recognition with Neural Networks”, NAACL 15

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Sample Test Utterances

# Method Transcription

(1)

Truth yeah i went into the i do not know what you think of fidelity but HMM-GMM yeah when the i don’t know what you think of fidel it even them CTC+CLM yeah i went to i don’t know what you think of fidelity but um

(2)

Truth no no speaking of weather do you carry a altimeter slash barometer

HMM-GMM no i’m not all being the weather do you uh carry a uh helped emitters last brahms her

CTC+CLM no no beating of whether do you uh carry a uh a time or less barometer

(3)

Truth i would ima- well yeah it is i know you are able to stay home with them

HMM-GMM i would amount well yeah it is i know um you’re able to stay home with them CTC+CLM i would ima- well yeah it is i know uh you’re able to stay home with them

Table 2: Example test set utterances with a ground truth transcription and hypotheses from our method (CTC+CLM) and a baseline HMM-GMM system of comparable overall WER. The words fidelity and barometer are not in the lexicon of the HMM-GMM system.

Figure 2: DBRNN character probabilities over time for a single utterance along with the per-frame most likely character string s and the collapsed output

(s). Due to space constraints we only show a dis- tinction in line type between the blank symbol and non-blank symbols.

GMM and DBRNN+NN-3 systems.

The DBRNN sometimes correctly transcribes OOV words with respect to our audio training cor- pus. We find that OOVs tend to trigger clusters of errors in the HMM-GMM system, an observation that has been systematically explored in previous work (Goldwater et al., 2010). As shown in ex- ample utterance (3), HMM-GMM errors can intro- duce word substitution errors which may alter mean- ing whereas the DBRNN system outputs word frag- ments or non-words which are phonetically similar and may be useful input features for SLU systems.

Unfortunately the Eval2000 test set does not offer a

rich set of utterances containing OOVs or fragments to perform a deeper analysis. The HMM-GMM and best DBRNN system achieve identical WERs on the subset of test utterances containing OOVs and the subset of test utterances containing fragments.

Finally, we quantitatively compare how character probabilities from the DBRNN align with phonetic segments from the HMM-GMM system. We gener- ate HMM-GMM forced alignments on a large sam- ple of the training set, and separate utterances into monophone segments. For each monophone, we compute the average character probabilities from the DBRNN by aligning the beginning of each mono- phone segment, treating it as time 0. We measure time using feature frames rather than seconds. Fig- ure 3 shows character probabilities over time for the phones k, sh, w, and uw.

Although the CTC model does not explicitly com- pute a forced alignment as part of training, we see significant rises in character probabilities corre- sponding to particular phones during HMM-GMM- aligned monophone segments. This indicates that the CTC model automatically learns a reasonable alignment of characters to the audio. Generally, the CTC model tends to produce character spikes to- wards the beginning of monophone segments. This is especially evident in plosive consonants such as k and t. For liquids and glides (r, l, w, y), the CTC model does not produce characters until later in the monophone segment. For vowels the CTC character

Image from Maas et al., “Lexicon Free Conversational Speech Recognition with Neural Networks”, NAACL 15

(30)

Analysis

5 0 5 10 15 20 25

0.05 0.10 0.15 0.20

0.25 k

c e k

5 0 5 10 15 20 25

0.05 0.10 0.15 0.20

0.25 sh

i h s t

5 0 5 10 15 20 25

0.05 0.10 0.15 0.20

0.25 w

w

5 0 5 10 15 20 25

0.05 0.10 0.15 0.20

0.25 uw

d o u y

Figure 3: Character probabilities from the CTC-trained neural network averaged over monophone segments created by a forced alignment of the HMM-GMM system. Time is measured in frames, with 0 indicating the start of the monophone segment. The vertical dotted line indicates the average duration of the monophone segment. We show only characters with non-trivial probability for each phone while excluding the blank and space symbols.

probabilities generally rise slightly later in the phone segment as compared to consonants. This may occur to avoid the large contextual variations in vowel pro- nunciations at phone boundaries. For certain conso- nants we observe CTC probability spikes before the monophone segment begins, as is the case for sh.

The probabilities for sh additionally exhibit multiple modes, suggesting that CTC may learn different be- haviors for the two common spellings of the sibilant sh: the letter sequence “sh” and the letter sequence

“ti”.

6 Conclusion

We presented an LVCSR system consisting of two neural networks integrated via beam search decod- ing that matches the performance of an HMM-GMM system on the challenging Switchboard corpus. We built on the foundation of Graves and Jaitly (2014) to vastly reduce the overall complexity required for LVCSR systems. Our method yields a complete first-pass LVCSR system with about 1,000 lines of code — roughly an order of magnitude less than high performance HMM-GMM systems. Operat- ing entirely at the character level yields a system which does not require assumptions about a lexicon

or pronunciation dictionary, instead learning orthog- raphy and phonics directly from data. We hope the simplicity of our approach will facilitate future re- search in improving LVCSR with CTC-based sys- tems and jointly training LVCSR systems for SLU tasks. DNNs have already shown great results as acoustic models in HMM-DNN systems. We free the neural network from its complex HMM infras- tructure, which we view as the first step towards the next wave of advances in speech recognition and language understanding.

Acknowledgments

We thank Awni Hannun for his contributions to the software used for experiments in this work. We also thank Peng Qi and Thang Luong for insightful dis- cussions, and Kenneth Heafield for help with the KenLM toolkit. Our work with HMM-GMM sys- tems was possible thanks to the Kaldi toolkit and its contributors. Some of the GPUs used in this work were donated by the NVIDIA Corporation. AM was supported as an NSF IGERT Traineeship Recipient under Award 0801700. ZX was supported by an NDSEG Graduate Fellowship.

Image from Maas et al., “Lexicon Free Conversational Speech Recognition with Neural Networks”, NAACL 15

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