ITERATIVE TRAINING OF A DPGMM-HMM ACOUSTIC UNIT RECOGNIZERIN A ZERO RESOURCE SCENARIO
Michael Heck, Sakriani Sakti, Satoshi Nakamura
Augmented Human Communication Laboratory,
Graduate School of Information Science,
Nara Institute of Science and Technology,
Nara, Japan{michael-h,ssakti,s-nakamura}@is.naist.jp

ABSTRACTIn this paper we propose a framework for building a fullfledged acoustic unit recognizer in a zero resource setting,i.e., without any provided labels. For that, we combine an iterative Dirichlet process Gaussian mixture model (DPGMM)
clustering framework with a standard pipeline for supervised
GMM-HMM acoustic model (AM) and n-gram language
model (LM) training, enhanced by a scheme for iterativemodel re-training. We use the DPGMM to cluster feature vectors into a dynamically sized set of acoustic units. The frame
based class labels serve as transcriptions of the audio data
and are used as input to the AM and LM training pipeline.
We show that iterative unsupervised model re-training of thisDPGMM-HMM acoustic unit recognizer improves performance according to an ABX sound class discriminability task
based evaluation. Our results show that the learned models
generalize well and that sound class discriminability benefits from contextual information introduced by the language
model. Our systems are competitive with supervisedly trained
phone recognizers, and can beat the baseline set by DPGMM
clustering.

been work on estimating popular feature transformations
without prior labels at hand [2, 3]. Much work has been doneon training and adapting speech recognizers with little to nosupervision [4, 5, 6]. Usually, automatic transcriptions for
new adaptation data are produced using speech recognizers
that were initialized on very small amounts of supervised
data. Transcribing and re-training is normally repeated overmultiple iterations to benefit from gradually improved models. Initial transcriptions can also be generated with only
segmental tokenizers at hand [7]. Where the underlying
sound inventory of the data is unknown, works such as [8, 9]
perform acoustic unit discovery and modeling.
Works like these raise an interesting question: Can wego one step further and build a state-of-the-art acoustic unit
recognizer for an unknown language given merely the audio
data without any provided labels or other linguistic knowledge? Answering this question demands solving the tasks of(1) finding an inventory of the underlying sounds of the target
language (2) modeling these sounds appropriately, preferably
along with contextual linguistic information that supports arecognizer in handling ambiguity. Possible applications ofsuch recognizers range from automatic transcription for language analysis and preservation, keyword discovery and topic
classification to providing a basis for developing functional
services for natural human-machine interaction.

Index Terms— acoustic unit discovery, Dirichlet process,unsupervised learning, unsupervised speech recognition, zero
resource
1. INTRODUCTION

Machine learning approaches to the first task are patternmatching [10, 11] on raw audio data and unsupervised soundunit detection [12]. These techniques have been successfully
applied to solve tasks such as spoken term detection [13],topic segmentation [14] or document classification [15].
Bayesian models such as the Dirichlet process Gaussian
mixture model (DPGMM) are a good choice for dealing withthe problem of unknown model complexity. Chen et al. [16]cluster speech features by inferring a DPGMM and demonstrate its suitability for automatic detection of sound classes
in untranscribed data. Their work is the best-performing contribution to the zero resource speech challenge 2015 [1]. We
demonstrated in previous work [17, 18] that it is possible to

We speak of a zero resource scenario in the speech processing
domain, when labeled training data and knowledge about thetarget language are not available. Current technology can not
yet imitate capacities that are natural to humans to robustly
learn acoustic and language models in an unsupervised way.
Recently, evaluations such as the zero resource speech challenge [1] specialize in tackling this demanding task by asking
the following question: Can we learn a whole language fromscratch by deploying adaptive machine learning algorithms?
The absence of supervision makes it difficult to apply machine learning methods that are commonly used to build stateof-the-art HMM based speech processing systems. There has

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unsupervisedly learn various feature transformations on automatically generated labels, and that these transformations canbe used to produce feature vectors that considerably improve
the DPGMM clustering performance.
In this work we further expand our unsupervised learning scheme in the zero resource scenario of our previous
studies. We propose to build a full-fledged acoustic unit recognizer without prior labels. For that, we combine our iterative DPGMM clustering framework with a standard pipeline
for supervised GMM-HMM acoustic model (AM) and ngram language model (LM) training, along with a scheme
for iterative model re-training. Specifically, we sample a
DPGMM to find a dynamically sized set of acoustic units thatare optimized with respect to sound class discriminability.
These acoustic units are used to initialize a context dependent
speaker adaptive AM and an acoustic unit based n-gram LM.
Similar to [7, 19] we follow an iterative approach attempting
to gradually improve the trained models by decoding and retraining, but we let the DPGMM sampler decide the amountand structure of the used sounds.
With our proposed framework it is possible to build a
DPGMM-HMM acoustic unit recognizer that is competitive
with supervisedly trained phone recognizers, according to
the performance on the ABX sound class discriminability
task [20]. The ABX test based evaluation measures classdiscriminability of posteriorgrams. This allows a direct comparison of the decoding quality with the clustering qualityof the DPGMM. We show that our DPGMM-HMM recognizer can beat the baseline set by our previous studies on
DPGMMs. We also show that the model re-training helps improve performance even over multiple iterations. Our results
indicate that the contextual information encapsulated in the
LM considerably helps the sound class discriminability. Useful models can be unsupervisedly learned even on minimal
amounts of data. We argue that by utilizing the DPGMMHMM framework it is possible to build a state-of-the-art
acoustic unit recognizer without any prior supervision.

DPGMM
clustering

Transformation
estimation

DPGMM
clusteringLabelsTransforms

LabelsABX
scoring

DPGMM-based Acoustic Unit Discovery
HMM-based Acoustic Unit Recognition
Supervised acoustic model training
Monophones

Triphones

Triphones
LDA+MLLT

Triphones
SAT

Language modeltraining
n-grams

iterations
Labels

Decoding

ABX
scoring

Fig. 1. Scheme of the DPGMM-HMM acoustic unit recognition framework. x1 · · · xn denotes the input feature vectors.
The model training for acoustic unit recognition is iterative,
where the models of iteration i = 1 are trained on the initiallabels from the acoustic unit discovery step, and the models
of iteration i ∈ {2, . . . , imax } are trained on the hypothesesof iteration i − 1. p̂1 · · · p̂n denotes the posteriorgrams after DPGMM sampling. pi1 · · · pin denotes the posteriorgrams
after decoding in iteration i.
2.1. Dirichlet process Gaussian mixture model
DPGMMs (also known as infinite GMMs) extend finite mixture models by the aspect of automatic model selection:
The model finds its complexity automatically given the data.
Inference is typically sample based using a Markov chain
Monte Carlo (MCMC) scheme such as Gibbs sampling. The
sampler used here alternates between a non-ergodic restricted
Gibbs sampler and a split/merge sampler to form an ergodic
MCMC sampler. A super-cluster sampler groups similarclusters into super-cluster groups g, given a cluster similarity
measure. The merge step of the split/merge sampler can beconditioned on g to only consider merge candidates within
the same super-cluster that the current sample belongs to.
For more in-depth informations regarding the used DPGMM
sampler, please refer to [16, 21].

2. ACOUSTIC UNIT DISCOVERY
To solve the task of acoustic unit discovery, we utilize a
DPGMM sampler to cluster extracted speech features into
various sound classes. The set size is determined dynamically by the Bayesian approach. Our method is based on [16],but has been modified by us in previous work to incorporate
automatically estimated linear feature transformations which
proved to be very helpful in constructing good features forboosting the clustering quality [17, 18]. Because many usefulfeature transformations need labels for estimation, we use a
multi-staged clustering framework that automatically finds
frame-based class labels in a first run of clustering standard
speech features, estimates feature transformations to transform these features and re-clusters the transformed input in asecond run. The clustering scheme is depicted in Figure 1.

2.2. Unsupervised speech feature transformation
Speech feature transformations used by our framework help
to project feature vectors into a more suitable sub-spacefor sound class discrimination by feature de-correlationand speaker adaptation. To estimate various transformations we train an AM by exploiting a standard pipeline forsupervised training. During the course of the training we

58

42:

15

learn transformations via linear discriminant analysis (LDA),
estimating maximum likelihood linear transforms (MLLT)
and using feature-space maximum likelihood linear regression (fMLLR). LDA helps to minimize intra-class discriminability and maximize inter-class discriminability of the
speech features and to enable dimensional reduction of highdimensional stacked feature vectors. The state-dependent
MLLTs maximize the likelihood of the target data. fMLLR
helps to capture inter-speaker variability in speaker dependent transforms and to generate speaker independent state
distributions instead.

42

95
23

15:

23:

...

Super-clusters:

Fig. 2. Left: Scheme of a sampled DPGMM. Super-clusters
are visualized with different line styles. Each Gaussian represents one sound class, denoted by a generic ID. Right:
DGPMM-HMMs trained on the DPGMM label output.

2.3. Two-stage clustering
We produce automatic labels by sampling an initial DPGMM
given standard feature vectors with their derivatives. The output consists of generic class labels and the hypothesized classmembership of every speech frame. Each class is simply
named with the numeric ID of the Gaussian that most likely
produces the respective feature vector.
The frame-wise labels serve as basis for the subsequenttransformation estimation. We collapse the labels for eachutterance to emulate a more natural textual reference by compressing all subsequent tokens of the same type to a single
token. We initialize an AM by context-independent monophone training. Then we subsequently train context dependent tri-phones on untransformed standard features. During
this model training we automatically learn LDA transformations using the acoustic states as classes. The MLLTs arelearned given the initialized HMMs, and fMLLR is based onalignments with speaker-independent features.

context-dependent tri-phones. The pre-processing produces
LDA+MLLT+fMLLR transformed feature vectors.
A commonly used topology for acoustic modeling is leftto-right 3-state HMMs with or without skip states because of
its suitability to model phone inventories crafted by linguists.
It is not guaranteed, however, that automatically discovered
acoustic units share the temporal properties of phones in the
linguistic sense. Thus, our setup is designed to also operatewith alternative HMM topologies.
The language model training produces an n-gram LM on
the same automatic transcriptions, where the transcriptions
are used as-is, i.e., no additional filtering or cleaning is performed prior to training. The LM is based on the class labels,
thus captures the phonotactics of the data, given the genericacoustic units.
The DPGMM sampler used to generate the automatic labels can sample labels that group several clusters according
to some cluster similarity measure. These super-cluster labels
can be used as an alternative to the normal cluster labels, thus
effectively reducing the amount of potential acoustic units tobe trained. Making use of this reduced set of classes makes
sense when the amount of clusters found during DPGMM
sampling is considerably higher than the size of commonlyused phone or sound inventories.

3. ACOUSTIC UNIT RECOGNITION
The automatic labels generated with the method describedabove can be used to train acoustic and language models fit
for decoding. This step uses the same standard pipeline forsupervised training as above, now with the objective to decode the target data with the resulting model in combination
with the LM. The acoustic unit recognition scheme is depicted
in Figure 1. The data sets we use in this zero resource setting
are the only resources we have for training and testing, thus
the entire training and decoding pipeline is designed for xfold cross-validation.

3.2. Decoding
The decoding is performed with the generic acoustic unit
based AM and LM, and in turn produces acoustic unit based
hypotheses, i.e., essentially resembling a “phone” recognizer.
Because naturally we do not have a development data set athand, we use default values for all parameters that might besubject to tuning, such as beam sizes and model weights.

3.1. Training
The acoustic model training makes use of automatic transcriptions that are produced by collapsing the class label
output from the multi-stage DPGMM clustering. The transcriptions are used to initialize context and speaker independent GMM-HMM monophone models, see Figure 2.
Multiple iterations of increasingly complex training followed by label writing result in speaker adaptively trained

3.3. Iterative re-training
A first system sys1 is initialized with the help of the transcriptions that were produced by formatting the DPGMM output.
By default, we iteratively re-train AM and LM simultaneously
by using the hypotheses produced with system sysi−1 to build

59system sysi in iteration i ∈ {2, . . . , imax }. The iterations after building system sys1 can alternatively be restricted to one
model type, i.e., either the AM or the LM is the sole subjectof iterative re-training.
It is straightforward to replace the transcriptions of theprevious training step with the hypotheses. Afer each iteration, we evaluate the system performance by extracting
frame-wise acoustic unit posteriorgrams and measuring their
ABX sound class discriminability.

Features
DPGMM ([16])DPGMM ([18])DPGMM-HMM (sup.)DPGMM-HMM

EnglishXitsonga
within across within across
10.8
16.3
9.6
17.2
10.6
15.7
8.4
12.2
12.5
16.6
6.7
11.2
11.1
15.1
8.2
11.6

Table 1. The baseline results provided by the DPGMM
clustering (DPGMM), the top-line result provided by the supervisedly trained phone recognizer, and the optimal resultsfor each condition given our proposed setup (both DPGMMHMM).

4. EXPERIMENTS
4.1. Datadistances. Our scores are the error rates within and acrossspeakers. The rates are averaged over all contexts for a given
pair of central phonemes and then over all pairs of central
phonemes.

We use the official data set of the Interspeech zero resource
speech challenge [1] for all our experiments, which contains
two separate data sets of pure speech for American English(4h 59min) and Xitsonga (2h 29min), a southern African
Bantu language. Each segment contains non-overlapping
speech of exactly one speaker and is without noise or pauses.
The English data is extracted from the Buckeye corpus andconsists of conversational speech. The Xitsonga data is an excerpt of the NCHLT corpus and is comprised of read speech.

4.3. Setup
For the feature vector clustering via DPGMM sampling, we
use the same initialization and parameters than in [17, 18].
We use the Kaldi speech recognition toolkit [23] to extract PLP speech feature vectors for a frame length of 25
milliseconds and frame shift of 10 milliseconds. Mean variance normalization (MVN) and vocal tract length normalization (VTLN) is applied. All AMs used in our framework are
likewise trained with Kaldi, following a standard scheme forspeaker adaptive training (Kaldi recipe s5). All parametersthat can be tuned are set to default values. To form the input for LDA estimation, we stack the standard PLP featureswith a context of 4, meaning that the 4 left and 4 right feature vectors are stacked on top of the current vector, which isthe center vector. The LDA output dimensionality is 20 forfeature transformation prior to DPGMM clustering, and set tothe default value 40 for the decoding. We use a either a modified 3-state HMM topology with a skip from the first state to
the next HMM, or a 1-state HMM topology.
To train the n-gram LMs for our experiments, we use the
SRILM toolkit [24] with Witten-Bell discounting [25] and nopruning. We set n = 4 for all decoding experiments.

4.2. Evaluation
The evaluation metric we use to measure the cluster quality
and the decoding quality is the ABX phone discriminability between phonemic minimal pairs [20], a method whichis related to the ABX task used in psycho-physics [22]. The
provided toolkit allows the easy evaluation of posteriorgrams
which we can extract after DPGMM clustering as well as after
decoding.
Each acoustic unit being found via DPGMM clustering(and used for acoustic modeling for the decoding approach)is considered a phone in the context of the evaluation. We
compute GMM posteriorgrams for each speech frame afterclustering as described in Section 2 and acoustic unit posteriorgrams after decoding as described in Section 3, and scorethem in the same manner. Both types of posteriorgrams sharethe same structure due to the fact that the sound units of the
AM are identical with the DPGMM classes.
Let A and B be speech representations of sound categories a and b. The ABX phone discrimination accuracy isc(a, b) =XX
1|a| · |b| · (|a| − 1)

4.4. Clustering transformed speech features
The baseline for the DPGMM based feature vector clustering
performance was set by Chen et al. [16], which won track oneof the zero resource speech challenge 2015 [1]. This system
has been outperformed by our clustering setup using featuretransformations as described in our previous work [17, 18].
We found that PLP feature vectors are consistently leading toa higher clustering quality than MFCC feature vectors. We
also found that the stacking context parameter c = 4 prior to
LDA transformation and LDA output dimensionality d = 20
are good values to work with. With the application of LDA

X

A∈a B∈b X∈a\{A}1(δd(A,X)<d(B,X) + δd(A,X)=d(B,X) )2(1)

where δ is an indicator function and d(·, ·) is the dynamic time
warping (DTW) distance defined over vectors of frame-based
features (in this case posteriors). As in Schatz et al. [20],
we use the Kullback-Leibler divergence to compute the DTW

60

12.0
11.8
11.6
11.4
11.2
11.0
10.8
10.6

Error rate

17.0
16.5
16.0
15.5
15.0

1

2

3
Iteration

4

5

12.8
12.6
12.4
12.2
12.0
11.8
11.6

English (error within speakers)

Error rate

Error rate

Error rate

English (error across speakers)

1

2

3
Iteration

4

5

9.2
9.0
8.8
8.6
8.4
8.2

Xitsonga (error across speakers)
Best DPGMM-PG
AM+LM re-training,
3-state HMMs
Normal cluster labels

1

2

3
Iteration

45

AM+LM re-training,
3-state HMMs
Super-cluster labels
AM+LM re-training,
1-state HMMs
Super-cluster labels

Xitsonga (error within speakers)

AM re-training,
1-state HMMs
Super-cluster labels

1

2

3
Iteration

4

5

LM re-training,
1-state HMMs
Super-cluster labels

Fig. 3. Error rates within and across speakers for both languages in dependency of the model training iteration. The black
horizontal line marks the baseline set by the best DPGMM clustering. AM re-training and LM re-training denote systems with
exclusively re-trained AM or LM, respectively. 1-state HMMs denotes systems that use the single state topology instead of thedefault. Systems have been trained either on the normal DPGMM label output or on the super-cluster labels.
we were able to produce feature vectors that considerably
helped the DPGMM clustering process to find better clusters.
Further, the transformations learned with fMLLR during the
speaker adaptive training helped boost the discrimination capabilities across speakers. The details of these findings can befound in [17, 18]. The performance of Chen et al.’s and our
setup is listed in Table 1.

recognizers is depicted in Figure 3. Even though a generaltendency to convergence is not observable, one can see thatmultiple iterations of model re-training tend to have a positiveeffect. The error across speakers drops for the recognizers forboth languages even after 3 or more iterations, whereas the
positive effect diminishes more rapidly within speakers.
The acoustic unit recognizers are competitive when compared to the supervisedly trained phone recognizers. For English, our proposed setup can even beat the supervised system
according to ABX discriminability within speakers.
The posteriorgrams after decoding start off with a higher
discriminability error than the posteriorgrams after DPGMM
sampling, which were used to generate the labels for the decoder training in the first place. In other words, a performanceloss is observable by attempting to train more complex models. However, a steady performance improvement is observable for the discriminability across speakers, while the error
rate within speakers remains relatively stable. We take thisas an indicator that the models do have the capacities of stilllearning more from the data.

4.5. Decoding with acoustic units
We trained an AM and a 4-gram LM given the classes discovered during the DPGMM clustering. Because training
and test data are identical in our scenario, we use 12-foldcross-validation for training the models for decoding. The
cross-validation ensures that the measured performance isan indicator of how well the learned models generalize, besides showing that they are generally capable of representing
the training data. The models are used to decode the crossvalidation left-out portion of the data. The decoding hypotheses were subsequently used to re-train the models for another
iteration of decoding. This was done multiple times to measure a potentially positive effect of iterative unsupervised
re-training on the decoder performance.
To get a top-line performance for the decoding withacoustic units, i.e., the kind of performance we can expect
if we had an optimal set of acoustic units and (near) perfecttranscriptions to learn models, we also trained a normal AM
and phone-based 4-gram LM with the same setup given theoriginal references and decoded the target data with 12-foldcross-validation. All results are listed in Table 1.
The performance of the DPGMM-HMM acoustic unit

4.6. Using super-cluster labels
The DPGMM sampler can sample labels that group several
clusters according to some cluster similarity measure, in thiscase the J-Divergence [26]. We used the super-cluster labels
as an alternative to the normal cluster labels to effectively
reduce the amount of potential acoustic units. The numberof clusters found during DPGMM sampling usually is in thehundreds, whereas the sampled super-clusters are in the rangeof tens, raising the hope that they resemble more phone-like61

units. As can be seen in Figure 3 we indeed observed a performance gain when training the models on super-cluster labels, supporting our assumption that the super-clusters might
be more suitable to describe the target data.

unsupervisedly discover acoustic units in the target data forsubsequent acoustic and language model training on automatically generated labels. The resulting DPGMM-HMM acoustic unit recognizer was used to solve the ABX sound class discriminability task. Multiple iterations of decoding and model
re-training proved to be suitable to boost performance. We
showed that the automatically discovered acoustic units seemto differ from phones in the sense that they seem generally
shorter. We demonstrated that the contextual informations
modeled by the LM considerably help discriminating sounds
and that the sound class discriminability after DGPMM clustering can be outperformed by introducing such contextual
knowledge. With our proposed framework it is possible tobuild a DPGMM-HMM acoustic unit recognizer that is competitive with supervisedly trained phone recognizers. Useful models can be unsupervisedly learned even on minimal
amounts of data. A recognizer build in this way without any
prior supervision can serve as basis for further and more sophisticated system development. In future work we plan toutilize such initialized systems to also infer lexical knowledge
from the data to boost recognition performance and to enable
automatic generation of lexica for new languages.

4.7. Modeling sounds with single states
The fact that we were not able to beat the DPGMM clustering in the ABX task lets us assume that the acoustic units
we found might not quite resemble phones as defined by linguistics. Thus we also conducted decoding experiments with
1-state HMMs instead of 3-state HMMs. By simplifying themodels in this way we observed a considerable performancegain. Apparently, the data can be represented more accurately
with chained single state HMMs. We take this as a sign thatthe found units are potentially too short to be modeled accurately with 3 states.
The posteriorgrams after decoding with 1-state HMMs
outperform the DPGMM posteriorgrams in all but the withinspeaker discriminability test for English. It is also noteworthy that the model training seems to saturate after fewer iterations than before, possibly due to the reduced complexityof the AM. We now see optimal performance after the third
iteration at the latest. The proposed system also clearly outperforms the supervisedly trained phone recognizer for English by showing a relative improvement of 9% to 11% insound class discriminability performance. For Xitsonga, theperformance of the automatic sound units is fairly close to theperformance of the supervisedly trained recognizer.

6. ACKNOWLEDGEMENTS
Part of this research was supported by JSPS KAKENHI Grant
Number 24240032 and 26870371.
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