A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition
Abstract
Inspired by the recent successes of deep generative models for Text-To-Speech (TTS) such as WaveNet and Tacotron [29, 33], this article proposes the use of a deep generative model tailored for Automatic Speech Recognition (ASR) as the primary acoustic model (AM) for an overall recognition system with a separate language model (LM).
Two dimensions of depth are considered: (1) the use of mixture density networks, both autoregressive and non-autoregressive, to generate density functions capable of modeling acoustic input sequences with much more powerful conditioning than the ﬁrst-generation generative models for ASR, Gaussian Mixture
Models / Hidden Markov Models (GMM/HMMs), and (2) the use of standard
LSTMs, in the spirit of the original tandem approach, to produce discriminative feature vectors for generative modeling.
Combining mixture density networks and deep discriminative features leads to a novel dual-stack LSTM architecture directly related to the RNN Transducer [8], but with the explicit functional form of a density, and combining naturally with a separate language model, using Bayes rule.
The proposed generative models are compared experimentally in terms of log-likelihood and frame accuracy.
Introduction 1.1 The channel model & compositional ASR
For most of its history the ﬁeld of ASR has used a collection of separate modules to represent different stages of an overall processing chain.
Fred Jelinek formalized this approach within his concept of the “noisy channel model” in which components chain together to form a consistent overall joint probability P ( X
W )
for e.g. a sequence of acoustic observations X and a sequence of words W .
In turn
P ( X
W ) enables the Bayes’ decision procedure based on the posterior P ( W | X ) , and Bayes’ theorem [14]:
[Equation]
where p ( X ) can be dropped from the recognition procedure, as it doesn’t affect the relative posteriors of hypotheses W .
This model was particularly convenient given the strong independence assumptions in the model structures used.
Gaussian Mixture Models (GMMs) used jointly with 1st-order Hidden
Markov Models (HMMs) were well-suited to this modular approach, as they directly provide an acoustic likelihood p ( X | W ) for a sequence of acoustic feature vectors X = x 1: T conditioned on a given sequence of modeling units, e.g. phonemes, graphemes or words, W = w 1: M .
Denoting a possible state alignment of W to X as S W = { w 1 , ..., w T } , the overall acoustic log-likelihood is deﬁned with strong independence assumptions, using
[Equation]
and a sum over alignments (computed efﬁciently using the forward-backward algorithm),
[Equation]
or the best state alignment (the Viterbi approximation):
[Equation]
This then combines naturally with a separate language model probability, P ( W ) to form the joint probability P ( X, W ) .
Other probabilistic modules such as a pronunciation dictionary can be introduced into the overall chain, again combining with the other components according to Bayes’ rule.
One can keep adding modules, or substituting modules, and the overall model still holds.
In this sense, this approach to
ASR is “compositional”.
1.2 Shoehorning discriminative models into the modular channel model: the hybrid model
The adoption of more powerful, discriminative models such as Deep Neural Networks (DNNs) and
Long Short Term Memory models (LSTMs) did not at ﬁrst alter this model.
The popular “hybrid” approach (still considered state-of-the-art by most of the community today) simply converts the posteriors P ( s | x ) (deﬁned at the level of a single observation x for a model state s ) obtained from
DNNs or LSTMs to a scaled likelihood by normalizing the posterior by a state prior P ( s ) [3].
Fleshing out this picture, the hybrid model for DNNs then is
[Equation]
and for a bidirectional LSTM is
[Equation]
These (scaled) likelihoods can then plug back into the channel model using Bayes rule, as before, and can also plug into an overall sequence training objective function such as utterance-level MMI or sMBR [19, 21].
Note that for the typical DNNs and LSTMs used by the community, sequence training has the potential to estimate implicitly the scaling term p ( s ) as an output unit bias term, so that it optimizes the sequence-level criterion.
The overall construction of a sequence-level training objective, converting local frame-level scores from a discriminative model into “generative” scaled likelihoods, only to then plug those into a discriminative sequence training criterion, may seem like a strange hybrid indeed.
Nonetheless, this has been a remarkably effective approach, that still constitutes the state-of-the-art [32].
1.3 Goodbye, modularity?
In contrast, end-to-end models such as Listen, Attend & Spell (LAS) [4] or RNN Transducer (RNNT) [8] dispense with the modular channel model altogether, and directly optimize a discriminative sequence-level model with a discriminative criterion.
By construction, these models are all-inone models that represent P ( W | X ) directly, with no chaining of sub-module probabilities.
The model directly deﬁnes the posterior needed for recognition.
If one has a large enough training set of supervised data pairs, this can be thought of as the perfect model.
State-of-the-art, or near state-of-the-art results have been reported for these models on challenging tasks [1, 5].
1.4 Combination of end-to-end models with other modules: Fusion
What has also been reported is the combination of end-to-end models with other models, e.g. an LM
P ∗ ( W ) trained on vastly more text data than is found in the transcripts of the audio data used to train P ( W | X ) .
Here the story is much less rosy.
One approach, sometimes referred to as “Shallow
Fusion”, is simple interpolation of the joint end-to-end model score with the LM score, e.g. [15]:
[Equation] [Equation]
The external LM has also been brought in through other methods, e.g. “Deep Fusion” [37] and “Cold Fusion” [24].
Though giving some practical gains, these are heuristic approaches with no clear mathematical justiﬁcation such as Bayes’ theorem.
1.5 Hybridization & Joint training
An additional approach to model combination, the “sequence version” of the hybrid model for ASR described above [3], is to form a separate estimate P ( W ) based only on the transcript-side of the {audio + transcript} data used to train P ( W | X ) , and use it to form a scaled likelihood of the acoustics given W :
[Equation]
related to p ( X | W ) by Bayes rule,
[Equation]
As the p ( X ) term doesn’t affect recognition, the scaled likelihood can then combine with P ∗ ( W ) for recognition using
P
[Equation]
following Bayes rule, but with some uncertainty whether the scaled likelihood is a meaningful representation of p ( X | W ) .
It remains to be seen whether this approach is more effective empirically than the fusion techniques.
As for the conventional ASR approach with discriminative acoustic models, the scaled likelihood can be plugged into an overall sequence training objective function such as utterance-level MMI or sMBR with a separate language model, if desired.
Depending on the model used for P ( W | X ) , note that sequence training has the potential to estimate implicitly the scaling term in Equation 8, so that it optimizes the sequence-level criterion.
1.6 A better way: a deep generative acoustic model
As described earlier, generative acoustic models offer a mathematically principled and interpretable approach to module combination, following Bayes rule.
While the ﬁrst-generation generative models used for ASR were an excellent ﬁt to the modular channel model, the strong independence assumptions of those early models severely hobbled their effectiveness, especially compared to the deep discriminative models, DNNs and LSTMs, that eventually replaced them [21, 23].
In contrast, generative models have made large advances in the area of speech synthesis.
State-of-the-art TTS approaches such as WaveNet and Tacotron [29, 33] speciﬁcally model p ( X | W ) as a fully-conditioned autoregressive model, using the entire unidirectional sequence of past observations x t (where x t is either an acoustic feature vector or a single scalar waveform sample), and typically, the entire sequence of symbols w i constituting the sequence W .
The “conditional WaveNet” model [29] exempliﬁes this well, deﬁning
[Equation]
where the input S W represents e.g. text and speaker features, and where observed samples x t are targets of e.g. an N -ways quantized softmax trained with CE, using e.g. a DNN with dilated convolutions.
Mixture density networks [2], based on either DNNs or RNNs, have also been effective as deep generative TTS models [35, 36].
Their use for ASR was proposed 20 years ago but not fully investigated [22].
Mixture density networks can use the same conditioning over observations and labels as the sample-by-sample WaveNet model, but use a Gaussian mixture density function:
[Equation] [Equation]
This is a reasonable choice when x t is a feature vector, as opposed to a scalar sample as in WaveNet.
As ASR models typically operate on feature vectors, and not at the sample level, mixture density networks may be a good ﬁrst step to investigate deep generative models for ASR.
1.7 ASR with a deep generative acoustic model: the deep channel model
The original channel model provides a principled foundation for ASR with deep generative acoustic models such as the TTS models just discussed [13, 22].
Whenever a likelihood p ( X | W ) is available,
ASR is possible - as long as a separate LM P ( W ) is available to form the joint probability P ( X, W ) , and given a decoder that can generate, score and prune different symbol sequence hypotheses W .
Time-synchronous beam-search can be used, among several possible decoding strategies.
The strong conditioning on the entire unidirectional symbol sequence history characterizing several of the models discussed here means that ASR decoding in principle cannot merge partial hypotheses, but that is also true for decoding with end-to-end ASR models such as LAS and RNN-T. Another approach to
ASR decoding with the models described here is N -best rescoring of a list of hypotheses from an existing conventional ASR system.
Note that the LM P ( W ) can of course be “deep” too, e.g. an
RNN-LM.
The “deep channel model” is just the channel model, with deep modules.
1.8 Extending the original Tandem approach
One generative approach to ASR that has shown lasting effectiveness is the tandem approach [7, 11], still yielding near state-of-the-art results [20, 28].
In contrast to the use of scaled likelihoods formed directly from the output of a discriminative model, described earlier, in the tandem approach, features from a penultimate layer are extracted from the discriminative model, and used in a separate generative model such as GMMs.
Typically these deep features are obtained from a bottleneck layer [9], concatenated with acoustic features, and transformed using e.g. Principal Components
Analysis (PCA) before being used as input to the generative model [28].
The result is a consistent generative model, but now operating on features that are far more discriminative than raw acoustic features by themselves.
Related work has investigated the embedding of a gaussian layer directly into the same DNN architecture, enabling joint discriminative training of feature extractor and gaussian density parameters [27, 31].
These studies limited themselves to DNNs for the discriminative feature extraction, and to standard GMMs for the generative model.
The approach proposed here extends this past work in both dimensions.
1.9 Towards integrated ASR & TTS, and semi-supervised training
Though not explored in this study, the goal of integrating or chaining ASR and TTS together has a long history [6, 13, 25, 26].
Recently, this has been used for semi-supervised ASR and TTS training [12, 26].
The models discussed here are clearly related to this body of work, as well as to the ideas for prediction-based unsupervised training discussed in [17].
1.10 Overview
This study explores two dimensions of depth for ASR-oriented generative models: the depth of the features being modeled, and the depth of the density function itself.
The features considered were:
• Shallow features: raw acoustic features, such as logmel energies or cepstral features; • Deep features: features obtained from the last layer of an LSTM stack trained discriminatively with a standard criterion such as CE, in the spirit of the original Tandem approach.
The density models considered were:
• Shallow density layers: vanilla gaussian mixture models, though implemented in TensorFlow and trained with SGD (using either ML or CE); • Deep density networks: mixture density density networks, both autoregressive and nonautoregressive, used to generate density functions capable of modeling acoustic feature vector sequences strongly conditioned on past features and labels, trained with ML.
Shallow mixture density models estimated using ML/SGD, modeling shallow features
Figure 1 illustrates the simplest of the models described here, a vanilla gaussian mixture,
[Equation]
(13) deﬁned for any label w m and any D -dimensional feature vector x t .
This likelihood makes strong independence assumptions; it is not conditioned on previous observations, nor on previous label symbols.
Nonetheless, it can plug into the complete likelihood of the utterance, based on Equation 2.
Implemented in TensorFlow, the parameters for this model are represented as TensorFlow Variables.
Standard deviations and mixing weights are represented in the log domain, with log_softmax() used to enforce the constraint that the mixing weights sum to one [31].
A simple radial covariance (using a single scalar standard deviation per mixture component) model was found to be convenient and effective:
[Equation]
where the log of the standard multi-variate gaussian pdf has been decomposed to emphasize the similarity to the typical DNN/LSTM ﬁnal layer, log ( softmax ( wx + b )) ; the main differences being the use of multiple mixture components, and more importantly, the self-normalized nature of the density function.
(See [10] for discussion of the equivalence of gaussian and log-linear models under certain conditions).
The model can be strengthened through a transformation A of the input feature x , where A is either a diagonal or lower-triangular matrix, with exponentially-transformed diagonal values to ensure the positivity of the matrix determinant.
The transformed feature vector Ax can then be used in Equation 14, corresponding to a gaussian mixture model with shared diagonal covariances or shared full covariances, overlaying the gaussian-speciﬁc radial model.
The results described in the main body of this article are all for densities using the simple radial model; results with stronger covariance models are described in Appendices A and B.
SGD-based optimization of the density function can be done with a number criteria; the natural ﬁt for this simple generative model is Maximum Likelihood (ML), but discriminative criteria such as Cross
Entropy (CE) or sequence-discriminative criteria such as utterance-level MMI or sMBR can also be used.
2.1 Verifying the effectiveness of ML/SGD training
ML-based estimation of GMMs was traditionally performed with the highly effective ExpectationMaximization (EM) algorithm [18].
In contrast to the EM algorithm, SGD is not speciﬁcally suited to
ML/GMM optimization, but its generality is highly appealing in the context of joint optimization of density functions with deep features or density parameters that are themselves generated by arbitrary, possibly recurrent neural network stacks.
The use of SGD for ML-based estimation of GMMs has not been widely reported.
A ﬁrst step in this study was to verify that ML/SGD can effectively learn the correct estimates given synthetic data drawn from known distributions.
For low-dimensional scenarios easily inspected visually, it was found that as long as the features were mean/standard-dev normalized, standard rules of thumb of GMM parameter initialization [34] led to surprisingly effective
ML/SGD estimation, even for mixtures with highly overlapping components, such as illustrated in
Figure 2.
For real-world data, two rough sanity checks were used: (1) TensorBoard histograms of mixing weights can diagnose unhealthy situations where a single gaussian component overwhelms all others in the mixture; (2) log-likelihoods on the training set should improve signiﬁcantly with increasing numbers of mixture components.
Careful comparison of ML/SGD-estimated GMMs with
EM-estimated GMMs, and schemes for mixture splitting in TensorFlow, could yield insights and better performance.
Autoregressive and non-autoregressive deep mixture density networks modeling shallow features
Figure 3 illustrates a mixture density network generated from an LSTM stack, predicting the next acoustic feature frame using all label symbols w 1: t up to that point in time (input to the LSTM stack via a class embedding), and in the autoregressive version of the model illustrated here, all previous acoustic features as well:
[Equation]
deﬁned for a speciﬁc alignment of labels w t to observations x t .
This likelihood can plug into
Equation 11, a much more powerful model than Equation 2.
In the non-autoregressive version, the mixture density network uses only the label symbols (and no previous acoustic features) to predict the next acoustic feature frame:
[Equation]
The power of these density networks is that the density function changes dynamically as a function of the input it is provided.
Via the input of the unidirectional embedded symbol sequence, the model can leverage long-span symbol context, a key feature when using non-standard acoustic modeling units such as graphemes.
The autoregressive version has the further feature that the ground truth of past observed acoustic feature vectors enables a kind of adaptation of the predictive model to the actual speech signal input to the model.
In contrast, the non-autoregressive model can only leverage the given unidirectional symbol context in making its predictions; it has to model all acoustic variability observed in the training set, across all speakers, speaking styles and acoustic environments.
3.1 Variants between autoregressive and non-autoregressive
Some variants on the full autoregressive model were considered:
• SH= n : size of the frame shift between observations input into the LSTM stack, and target of the prediction, if different from 1 .
E.g., SH=3 refers to predicting x t from x 1: t − 3 .
• BN= n : size of linear bottleneck on features before being fed into the LSTM stack, if any.
• ST= n : size of the stride over the frames input into the LSTM stack, if different from 1 .
E.g. ST=10 refers to only using every 10 -th frame, feeding in 0 s in between.
These are ways to provide less than the full past input to the autoregressive model.
One can expect that there would be a worsening of prediction log-likelihood, but perhaps an improvement in frame accuracy.
Note that “Professor Forcing” [16] was proposed to address the related problem of “Teacher
Forcing” in autoregressive models.
Shallow mixture density layer modeling deep discriminative features
Figure 4 illustrates a shallow density model of deep features.
The deep features are obtained from an
LSTM stack, separately trained with CE as a discriminative acoustic encoder.
The deep features in question are simply the output of the last LSTM layer, before the logits and softmax layer [30].
In contrast to previous tandem work using DNNs [9, 31], no bottleneck layer was found to be necessary, presumably due to the more compact LSTM layer size (e.g. 512 for LSTMs in this study, vs 2048 for the DNNs in [31]).
The model here is:
[Equation]
If the LSTM stack implementing the discriminative acoustic encoder d () is frozen, this is just a vanilla GMM layer that can be trained with ML, whose features happen to be highly discriminative.
Joint training of the density function and the feature extractor d () using ML is feasible, but requires proper handling of the Jacobian of the inverse of d () .
Joint training using CE, or utterance-level sMBR/MMI, however, has no such issue [20, 27, 31].
Deep mixture density networks modeling deep discriminative features
Finally, one can apply a deep density network to the modeling of deep features, as illustrated in
Figure 5.
The result is an architecture closely mirroring the well-known RNN Transducer [8], but explicitly formulated as a density function generated from an LSTM stack encoding a label sequence, applied to deep features encoding the acoustic sequence.
The resulting likelihood is:
[Equation]
If the LSTM stack encoding the deep features is frozen, this is a straightforward mixture density network that can be trained with ML, but with highly discriminative features.
As with the shallow density model of deep features, it can be trained jointly using discriminative criteria; and joint training with ML is again feasible but requires proper handling of the Jacobian of the inverse of d () .
The labels w can be input time-synchronously (repeating input labels for the length of their frame alignment) as well as label-synchronously, only inputting label transitions; the latter approach produced better frame accuracies, and was used in the experiments for this model.
To our knowledge, this is a novel application of mixture density networks to the modeling of deep features.
Experiments
The goal of the experiments was to verify that the added conditioning of the mixture density networks examined indeed improves log-likelihoods as expected, and to get some insights about likely WER from frame accuracies for these models.
6.1 Computation of frame accuracy
Frame accuracy for the shallow density layers (whether they use shallow or deep features) is straightforward to compute in TensorFlow, as N label class outputs for any given feature vector x t can easily be made available by batching Equation 13 or Equation 17, and compared with the output for the ground-truth label at time t .
For the deep density networks, in principle only one label class output is generated at a time, from the input of a speciﬁc embedded class label.
Frame classiﬁcation for density networks can be computed by producing N density outputs for N class label hypotheses input to the LSTM stack.
In all experiments here, a simple label class prior P ( s ) is estimated in-network [31] and used jointly with the likelihood produced by the density model to make the frame classiﬁcation.
(Though one could use in-network estimation of much more powerful (e.g. LSTM-based) LMs to tremendously boost frame accuracy, the simple class prior was deemed ﬁt to provide insight into the discriminative power of the AM itself).
An issue for deep density networks is the label context to use when measuring frame accuracy.
In a real ASR decoding scenario, multiple partial label sequences would be hypothesized by the decoder.
The approach adopted here was to measure frame accuracy for the density networks via conditioning on the ground-truth label sequence up to time t − 1 , with only the label at time t being hypothesized.
This can be computed efﬁciently in TensorFlow by batching the input of the N label hypotheses at all times t , but splicing into the inference pass a set of separately generated LSTM states up to t −obtained from ground-truth only input.
Figure 6 illustrates the use of class input-batching to do this efﬁciently in TensorFlow.
[Table] [Table]
6.2 Datasets
The dataset set used here is a set of 20000 hours of spontaneous speech from anonymized, humantranscribed Voice Search data for American English (en-us) .
The training examples are sequences of unstacked (single-frame) feature vectors extracted from a 64 ms window shifted over the audio samples in 30 ms increments.
The acoustic feature vectors used in the “deep feature” models, based on discriminative acoustic encoder architectures, are 256 dimensional logmel energies; those used in the “shallow feature” models are 96 dimensional cepstral coefﬁcients, consisting of a stack of 32+32+ mel ﬁlterbank cepstral coefﬁcients (MFCCs) and their ﬁrst and second derivatives, extracted from the same 64 ms windowing scheme.
Compared to logmel features, MFCCs are a reasonable choice when using a radial or diagonal covariance model, and the time derivatives provide a representation of temporal context [22].
Results for shallow feature density networks using logmels are reported in Appendices A and B; the frame accuracies detailed there are signiﬁcantly lower than those for
MFCCs with derivatives described in the following.
The overall training architecture closely follows the TensorFlow based implementation described in [32].
Each training step has a batch size of 64 example utterances, with a ﬁxed static LSTM unrolling of 20 frames.
Given the total batch size of 1280 frames, 1 epoch of training over the 20000 hour training set corresponds to 1.875M training steps.
For every conﬁguration, at least 10 epochs of
SGD training were run.
All LSTM stacks used are 5 layer models with 512 units each, for a total of roughly 4.3M parameters, not counting the ﬁnal layer, whose size will depend on the number of output classes and, for the density models, on the number of mixture components.
The class outputs are 42 context-independent (CI) phonemes or 8192 decision-tree clustered context-dependent (CD) phonemes.
A label class embedding of size 128 was used for all experiments.
CE training of the density models combines the acoustic likelihood output by the density model with an in-network estimate of the state prior P ( s ) (as in used in the computation of frame accuracy) [31].
The statistics reported were somewhat unscientiﬁcally obtained from smoothed TensorBoard plots when the models were deemed to have converged.
The smoothing factor for the TensorBoard moving average was 0.95.
Frame accuracies are based on a small held out dataset of 1000 utterances (with an average of 120 feature frames per utterance) and are hence quite noisy; only 2 signiﬁcant digits are reported for them.
The costs (negative log-likelihoods) are smoothed costs for the training criterion at convergence.
As the costs are running averages over the entire training set, they are less noisy than the frame accuracy statistics.
These metrics are admittedly not very rigorous, but nonetheless offer insights into the conditions explored.
As roughly 27% of the data is silence, a frame accuracy of anything not surpassing that can ofﬁcially be considered abysmal.
6.3 Results
Table 1 describes the costs (negative log-likelihoods) and frame accuracies for all models using shallow mel-cepstral features.
Shallow density with shallow features: The frame classiﬁcation of an ML-trained radial gaussian mixture barely surpasses abysmal levels, at 35%.
In contrast, a reference 5 -layer DNN with a matching number of parameters is at 55%.
However, CE-training the gaussian mixture model brings the frame accuracy up to 48%.
This may be a reasonable result, conﬁrming that the ﬂat structure of the GMM is not as effective a classiﬁer as a deeply structured DNN, but isn’t completely off either, if trained discriminatively.
The cost of 127 provides a baseline for the deep density models in the rest of Table 1.
Those models are expected to improve signiﬁcantly over that.
Deep autoregressive densities with shallow features: As expected, the deep density models do much better in terms of cost than the shallow density model just discussed.
Also as expected, the autoregressive models have better log-likelihoods than the non-autoregressive models, and furthermore, the full autoregressive models do better than the autoregressive models whose input was limited via the schemes for prediction shift ( SH ), feature bottleneck ( BN ) and frame stride ( ST ).
However, the trend for frame accuracy is the opposite.
The more past input is provided to the
LSTM stack generating the mixture density, the worse the frame accuracy.
One perspective is that providing too much information about past observations makes the prediction problem too easy, the label class information is not necessary, and hence the prediction is not discriminative.
(Professor
Forcing [16] may provide a solution to this problem).
Results are also shown for the unsupervised versions ( Unsup ), where all class input is merged into a single symbol.
One expects that the label class information would help improve the prediction cost over the unsupervised prediction, but that is not the case for the CI phone scenario, corresponding to the “CI AR” result, with a cost of 43.5, compared to the “Unsup AR” cost of 43.0.
The “CD AR” model does a bit better with a cost of 42.3.
Shifting the prediction target 3 frames into the future ( SH=3 ), however, produces a gain for the
CI model over the unsupervised version, 116 vs 118.
(See Appendix B for a deeper exploration of the effect of shifting the prediction target).
Going from 10 mixture components to 256 signiﬁcantly improves prediction cost, but doesn’t signiﬁcantly affect the frame accuracies for the autoregressive models here.
Deep non-autoregressive densities with shallow features: Removing all past observation input makes the prediction cost much worse than for the autoregressive models, but the frame accuracies are much better.
They are however still not competitive with the simple DNN, and in fact a bit worse than the
CE-trained shallow density model.
Table 2 ups the ante on frame accuracy, via the use of deep features.
A standard CE-trained LSTM is listed at the top of the table for reference.
Note that CI or CD phones were used consistently for both the deep features and the deep density.
Shallow densities with deep features, joint training with CE: Given the form of the radial density deﬁned in Equation 14 it should not surprise us that the classiﬁcation capacity of an LSTM stack using the radial density as the last layer, trained discriminatively, would closely match the classiﬁcation capacity of an LSTM stack with a standard logits layer.
This is what we see for both CI and CD versions, with a single radial component per mixture, achieving frame accuracies of 87% and 79% respectively, matching the standard CI and CD LSTMs.
Shallow densities with deep features, ML training: All the ML results here require pre-training of the standard LSTM (either CI or CD) with CE ﬁrst.
There is a clear drop in performance compared to the pure CE result, especially for the CD case, with 8192 output classes to discriminate compared to 42 .
However, the deep features here are not mean/standard-dev normalized (nor PCA’d, etc.).
The simple radial covariance model clearly could be improved upon for this particular conﬁguration, if desired.
Deep densities with deep features: The dual LSTM stack architecture does quite well as a predictor of the deep features, using just the label class context.
The prediction cost (e.g. -400 for thecomponent mixture version) is much better than the cost (-305) for the shallow density model of the same deep features, and in fact has signiﬁcantly better frame accuracy too, 85% vs 77%.
It may be argued that the deep features here have essentially extracted phoneme information from the CE training of the standard LSTM stack, and that now the prediction problem is really a label prediction problem, given the previously observed label class input.
However, there is nothing wrong with that from an ASR point of view, as the deep features are a function of the acoustics.
Furthermore, this may be exactly what is interesting about this last conﬁguration: using an encoding of a label sequence, we are trying to predict an encoding of an acoustic sequence.
Visualization of predictive quality for deep densities with shallow logmel features: See Appendix C for plots of generated logmel spectra, speciﬁcally focusing on the difference between supervised and unsupervised prediction quality as a function of the prediction shift (“SH”).
Summary
Four generative models were described and evaluated, the cartesian product of {shallow, deep} features and {shallow, deep} densities.
This recapitulates but also extends the original tandem approach to ASR.
As no WER evaluations were run, these results are rather tentative, but do give some insights into the models proposed.
The log-likelihood results follow our intuitions about the strength of the models.
The weak result for supervised vs unsupervised may suggest an issue with the experimental setup, e.g. the ﬁxed alignments used are poor, or it could reﬂect the weakness of the radial covariance model.
The somewhat better result for the CD phone model suggests either that the CI label context is not being completely represented by the LSTM state, or that “future blindness” (not knowing the identity of the immediately following phoneme) is a signiﬁcant issue, though other strategies such as re-alignment or decision delaying could address that too.
Appendix
A and Appendix B explore the strength of the covariance model used, and the difference between supervised and unsupervised models, in greater depth.
The frame accuracies for the models using shallow acoustic features seem too low to be useful for
ASR.
One could argue, however, that the autoregressive models, properly used in an ASR decoding framework, may perform much better than the frame accuracies suggest.
The difference in prediction cost between supervised and unsupervised scenarios discussed earlier (and in Appendices A and B) may be a better metric than frame accuracy for getting insight into ASR performance.
The use of deep features solves many of the problems generative models have in modeling environmental and speaker variability, and immediately provides strong discriminative power as measured by frame accuracy, but it may be seen as cheating and no longer purely generative.
Given their state-of-the-art frame accuracy, presumably the corresponding generative models described here would be viable for ASR.
The question then is, How does the generative nature of these models help us?
E.g. Does it allow for better compositionality with separate LMs, as claimed in the Introduction?
Does it provide the advantages attributed to generative models regarding unsupervised training or adaptation?
Full ASR decoding experiments with WERs are needed to address those questions.
Acknowledgments
The author thanks Tom Bagby, Matt Shannon, Ehsan Variani, Michiel Bacchiani, Khe Chai Sim, Arun
Narayanan, David Rybach, John Hershey, Heiga Zen, Mike Schuster, Samy Bengio, Tara Sainath,
Bo Li, Rohit Prabhavalkar, Ananya Misra, Patrick Nguyen, Navdeep Jaitly, Shankar Kumar, Mitch
Weintraub, Rif A. Sauros, Hasim Sak and Trevor Strohman for practical and conceptual help with this study.
Appendix A: Evaluation of different covariance models for autoregressive density networks modeling shallow logmel features
This Appendix describes the effect of covariance models stronger than the radial model used in the main body of the article, evaluated on logmel features modeled by autoregressive density networks.
Only a single gaussian per mixture is used for all radial (“Rad”), diagonal (“Diag”) and full (“Full”) covariance models.
The same prediction shift (“SH”) of 2 frames is used.
(A shift of 2 frames ensures there is nearly no overlap between input frames and prediction target frame, given the 64 ms audio analysis window size and 30 ms window step size).
Costs and frame accuracies for these models are shown in Table 3.
One can see that there is no big difference in cost between the radial and diagonal covariance models, but a big improvement is observed for the use of full covariance models.
The difference between supervised and unsupervised models, also reported here, is not clearly correlated with the strength of the covariance model.
The improvements in prediction cost do not improve the low frame accuracy observed for the logmel density models, but as discussed in the main body of the article, the frame accuracy metric used may be misleading.
[Table]
Appendix B: Evaluation of target prediction shift for autoregressive density networks modeling shallow logmel features
This Appendix describes the effect of different target prediction shifts (“SH”) for autoregressive density networks modeling logmel features, focusing on the difference between supervised and unsupervised models.
The results are shown in Table 4.
One can see that (1) the prediction cost is substantially worse for the larger prediction shifts, (2) the difference between supervised and unsupervised is substantially larger
[Table]
and (3) no real impact is observed on frame accuracy.
[Table]
1 / phone 4.5M 29%
Appendix C: Visualization of predicted vs. target logmel feature vectors using autoregressive density networks with different prediction target shifts
This Appendix uses logmel spectrograms to illustrate the behavior of both unsupervised and supervised autoregressive deep density networks trained to predict logmel features with different prediction target shifts.
Radial deep density networks were trained with a single gaussian per mixture, and the sequence of generated mean vectors plotted over time for unseen data from a separate dataset of read speech, alongside the actual target logmel feature vectors for the same data.
The ﬁgures here illustrate target features and generated means for individual data mini-batches, i.e. 64 concatenated segments of 20 frames each, used in the truncated LSTM unrolling scheme described in Section 6.2.
For a prediction shift of 1 frame (corresponding to the “SH=1” results for the radial model in Table 4), there is no visually discernible difference between unsupervised and supervised prediction, illustrated in Figure 7 and Figure 8, respectively; the label encoding is not necessary to make a good prediction.
(A shift of 1 frame corresponds to a nearly 50% overlap in analysis window, given the 64 ms window and 30 ms window step).
This behavior matches the rather small advantage in prediction cost observed for the supervised model over the unsupervised model in the corresponding “SH=1” results in Table 4.
In contrast, a shift of 4 frames makes the unsupervised prediction quite poor (Figure 9), and there is a noticeable advantage for the supervised prediction, which can leverage the label encoding (Figure 10).
These plots intuitively match the much better prediction cost for the supervised model compared to the unsupervised model for the corresponding “SH=4” results in Table 4.