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Bidirectional LSTM Networks for Improved Phoneme
Classification and Recognition
Alex Graves1, Santiago Fern´andez1,andJ¨urgen Schmidhuber1,2
1IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland
{alex, santiago, juergen}@idsia.ch
2TU Munich, Boltzmannstr. 3, 85748 Garching, Munich, Germany
Abstract. In this paper, we carry out two experiments on the TIMIT speech cor-
pus with bidirectional and unidirectional Long Short Term Memory (LSTM) net-
works. In the first experiment (framewise phoneme classification) we find that
bidirectional LSTM outperforms both unidirectional LSTM and conventional Re-
current Neural Networks (RNNs). In the second (phoneme recognition) we find
that a hybrid BLSTM-HMM system improves on an equivalent traditional HMM
system, as well as unidirectional LSTM-HMM.
1 Introduction
Because the human articulatory system blurs together adjacent sounds in order to pro-
duce them rapidly and smoothly (a process known as co-articulation), contextual infor-
mation is important to many tasks in speech processing. For example, when classifying
a frame of speech data, it helps to look at the frames after it as well as those before —
especially if it occurs near the end of a word or segment. In general, recurrent neural
networks (RNNs) are well suited to such tasks, where the range of contextual effects is
not known in advance. However they do have some limitations: firstly, since they pro-
cess inputs in temporal order, their outputstend to be mostly based on previous context;
secondly they have trouble learning time-dependencies more than a few timesteps long
[8]. An elegant solution to the first problem is provided by bidirectional networks [11,1].
In this model, the input is presented forwards and backwards to two separate recurrent
nets, both of which are connected to the same output layer. For the second problem, an
alternative RNN architecture, LSTM, has been shown to be capable of learning long
time-dependencies (see Section 2).
In this paper, we extend our previous work on bidirectional LSTM (BLSTM) [7]
with experiments on both framewise phoneme classification and phoneme recognition.
For phoneme recognitionwe use the hybrid approach, combining Hidden Markov Mod-
els (HMMs) and RNNs in an iterative training procedure (see Section 3). This gives us
an insight into the likely impact of bidirectional training on speech recognition, and also
allows us to compare our results directly with a traditional HMM system.
2LSTM
LSTM [9,6] is an RNN architecture designed to deal with long time-dependencies. It
was motivated by an analysis of error flow in existing RNNs [8], which found that long
W. Duch et al. (Eds.): ICANN 2005, LNCS 3697, pp. 799–804, 2005.
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Springer-Verlag Berlin Heidelberg 2005
800 A. Graves, S. Fern´andez, and J. Schmidhuber
time lags were inaccessible to existing architectures, because the backpropagated error
either blows up or decays exponentially.
An LSTM hidden layer consists of a set of recurrently connected blocks, known as
memory blocks . These blocks can be thought of a differentiable versionof the memory
chips in a digital computer. Each of them contains one or more recurrently connected
memory cells and three multiplicative units - the input, output and forget gates - that
provide continuous analogues of write, read and reset operations for the cells. More
precisely,the input to the cells is multiplied by the activation of the input gate, the output
to the net is multiplied by the output gate, and the previous cell values are multiplied by
the forget gate. The net can only interact with the cells via the gates.
Some modifications of the original LSTM training algorithm wererequired for bidi-
rectional LSTM. See [7] for full details and pseudocode.
3 Hybrid LSTM-HMM Phoneme Recognition
Hybrid artificial neural net (ANN)/HMM systems are extensively documented in the
literature (see, e.g. [3]). The hybrid approach benefits, on the one hand, from the use of
neural networks as estimators of the acoustic probabilities and, on the other hand, from
access to higher-level linguistic knowledge, in a unified mathematical framework.
The parameters of the HMM are typically estimated by Viterbi training [10], which
also provides new targets (in the form of a new segmentation of the speech signal) to
re-train the network. This process is repeated until convergence. Alternatively, Bourlard
et al. developed an algorithm to increase iteratively the global posterior probability
of word sequences [2]. The REMAP algorithm, which is similar to the Expectation-
Maximization algorithm, estimates local posterior probabilities that are used as targets
to train the network.
In this paper, we implement a hybridLSTM/HMM system based on Viterbi training
compare it to traditional HMMs on the task of phoneme recognition.
4 Experiments
All experiments were carried out on the TIMIT database [5]. TIMIT contain sentences
of prompted English speech, accompanied by full phonetic transcripts. It has a lexicon
of 61 distinct phonemes. The training and test sets contain 4620 and 1680 utterances
respectively. For all experiments we used 5% (184) of the training utterances as a vali-
dation set and trained on the rest.
We preprocessed all the audio data into frames using 12 Mel-Frequency Cepstrum
Coefficients (MFCCs) from 26 filter-bank channels. We also extracted the log-energy
and the first order derivatives of it and the other coefficients, giving a vector of 26
coefficients per frame in total.
4.1 Experiment 1: Framewise Phoneme Classification
Our first experimental task was the classification of frames of speech data into
phonemes. The targets were the hand labelled transcriptions provided with the data,
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition 801
Reverse Net Only
Forward Net Only
sil sil f ay vsil w ah n ow
Bidirectional Output
Target
one oh five
sil
Fig. 1. A bidirectional LSTM net classifying the utterance ”one oh five” from the Numbers95 cor-
pus. The different lines represent the activations (or targets) of different output nodes. The bidi-
rectional output combines the predictions of the forward and reverse subnets; it closely matches
the target, indicating accurate classification. To see how the subnets work together, their contri-
butions to the output are plotted separately (“Forward Net Only” and “Reverse Net Only”). As
we would expect, the forward net is more accurate. However there are places where its substitu-
tions (‘w’), insertions (at the start of ‘ow’) and deletions (‘f’) are corrected by the reverse net. In
addition, both are needed to accurately locate phoneme boundaries, with the reverse net tending
to find the starts and the forward net tending to find the ends (‘ay’ is a good example of this).
and the recorded scores were the percentage of frames in the training and test sets for
which the output classification coincided with the target.
We evaluated the following architectures on this task: bidirectional LSTM
(BLSTM), unidirectional LSTM (LSTM), bidirectional standard RNN (BRNN), and
unidirectional RNN (RNN). For some of the unidirectional nets a delay of 4 timesteps
was introduced between the target and the current input — i.e. the net always tried to
predict the phoneme of 4 timesteps ago. For BLSTM we also experimented with dura-
tion weighted error, where the error injected on each frame is scaled by the duration of
the current phoneme.
We used standard RNN topologies for all experiments, with one recurrently con-
nected hidden layer and no direct connections between the input and output layers.
The LSTM (BLSTM) hidden layers contained 140 (93) blocks of one cell in each, and
the RNN (BRNN) hidden layers contained 275 (185) units. This gave approximately
100,000 weights for each network.
802 A. Graves, S. Fern´andez, and J. Schmidhuber
All LSTM blocks had the following activation functions: logistic sigmoids in the
range [−2,2] for the input and output squashing functions of the cell , and in the range
[0,1] for the gates. The non-LSTM net had logistic sigmoid activations in the range
[0,1] in the hidden layer.
All nets were trained with gradient descent (error gradient calculated with Back-
propagation Through Time), using a learning rate of 10−5and a momentum of 0.9.At
the end of each utterance, weight updates were carried out and network activations were
reset to 0.
As is standard for 1 of K classification, the output layers had softmax activations,
and the cross entropy objective function was used for training. There were 61 output
nodes, one for each phonemes At each frame, the output activations were interpreted
as the posterior probabilities of the respective phonemes, given the input signal. The
phoneme with highest probability was recorded as the network’s classification for that
frame.
4.2 Experiment 2: Phoneme Recognition
A traditional HMM was developed with the HTK Speech Recognition Toolkit (http://
htk.eng.cam.ac.uk/). Both context independent (mono-phone) and context dependent
(tri-phone) models were trained and tested. Both were left-to-right models with three
states. Models representing silence (h#, pau, epi) included two extra transitions: from
the first to the final state and vice versa, in order to make them more robust. Observation
probabilities were modelled by eight Gaussian mixtures.
Sixty-one context-independent models and 5491 tied context-dependent models
were used. Context-dependent models for which the left/right context coincide with
the central phone were included since they appear in the TIMIT transcription (e.g. “my
eyes” is transcribed as /m ay ay z/). During recognition, only sequences of context-
dependent models with matching context were allowed.
In order to make a fair comparison of the acoustic modelling capabilities of the
traditional and hybrid LSTM/HMM, no linguistic information or probabilities of partial
phone sequences were included in the system.
For the hybrid LSTM/HMM system, the following networks (trained in the previ-
ous experiment) were used: LSTM with no frame delay, BLSTM and BLSTM trained
with weighted error. 61 models of one state each with a self-transition and an exit tran-
sition probability were trained using Viterbi-based forced-alignment. Initial estimation
of transition and prior probabilities was done using the correct transcription for the
training set. Network output probabilities were divided by prior probabilities to obtain
likelihoods for the HMM. The system was trained until no improvement was observed
or the segmentation of the signal did not change. Due to time limitations, the networks
were not re-trained to convergence.
Since the output of both HMM-based systems is a string of phones, a dynamic
programming-based string alignment procedure (HTK’s HResults tool) was used to
compare the output of the system with the correct transcription of the utterance. The
accuracy of the system is measured not only by the number of hits, but also takes into
account the number of insertions in the output string (accuracy = ((Hits - Insertions) /
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition 803
Total number of labels) x100%). For both the traditional and hybridsystem, an insertion
penalty was estimated and applied during recognition.
5Results
From Table 1, we can see that bidirectional nets outperformed unidirectional ones in
framewise classification. From Table 2 we can also see that for BLSTM this advantage
carried over into phoneme recognition.
Overall, the hybrid systems outperformed the equivalent HMM systems on
phoneme recognition. Also, for the context dependent HMM, they did so with far fewer
trainable parameters.
The LSTM nets were 8 to 10 times faster to train than the standard RNNs, as well
as slightly more accurate. They were also considerably more prone to overfitting, as
can be seen from the greater difference between their training and test set scores in
Table 1. The highest classification score we recorded on the TIMIT training set with a
bidirectional LSTM net was 86.4% — almost 17% better than we managed on the test
set. This degree of overfitting is remarkable given the high proportion of training frames
to weights (20 to 1, for unidirectional LSTM). Clearly, better generalisation would be
desirable.
Using duration weighted error slightly decreased the classification performance of
BLSTM, but increased its recognition accuracy. This is what we would expect, since its
effect is to make short phones as significant to training as longer ones [4].
Table 1. Framewise Phoneme Classification
Network Training Set Test Set Epochs
BLSTM 77.4% 69.8% 21
BRNN 76.0% 69.0% 170
BLSTM Weighted Error 75.7% 68.9% 15
LSTM (4 frame delay) 77.5% 65.5% 33
RNN (4 frame delay) 70.8% 65.1% 144
LSTM (0 frame delay) 70.9% 64.6% 15
RNN (0 frame delay) 69.9% 64.5% 120
Table 2. Phoneme Recognition Accuracy for Traditional HMM and Hybrid LSTM/HMM
System Number of parameters Accuracy
Context-independent HMM 80 K 53.7 %
Context-dependent HMM >600 K 64.4 %
LSTM/HMM 100 K 60.4 %
BLSTM/HMM 100 K 65.7 %
Weighted error BLSTM/HMM 100 K 66.9 %
804 A. Graves, S. Fern´andez, and J. Schmidhuber
6Conclusion
In this paper, we found that bidirectional recurrent neural nets outperformed unidirec-
tional ones in framewise phoneme classification. We also found that LSTM networks
were faster and more accurate than conventional RNNs at the same task. Furthermore,
we observed that the advantage of bidirectional training carried over into phoneme
recognition with hybrid HMM/LSTM systems. With these systems, we recorded bet-
ter phoneme accuracy than with equivalent traditional HMMs, and did so with fewer
parameters. Lastly we improved the phoneme recognition score of BLSTM by using a
duration weighted error function.
Acknowledgments
The authors would like to thank Nicole Beringer for her expertadvice on linguistics and
speech recognition. This work was supported by SNF, grant number 200020-100249.
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