Monolingual and crosslingual comparison of tandem features derived from articulatory and phone MLPs

In recent years, the features derived from posteriors of a multilayer perceptron (MLP), known as tandem features, have proven to be very effective for automatic speech recognition. Most tandem features to date have relied on MLPs trained for phone classification. We recently showed on a relatively small data set that MLPs trained for articulatory feature classification can be equally effective. In this paper, we provide a similar comparison using MLPs trained on a much larger data set - 2000 hours of English conversational telephone speech. We also explore how portable phone- and articulatory feature- based tandem features are in an entirely different language - Mandarin - without any retraining. We find that while phone-based features perform slightly better in the matched-language condition, they perform significantly better in the cross-language condition. Yet, in the cross-language condition, neither approach is as effective as the tandem features extracted from an MLP trained on a relatively small amount of in-domain data. Beyond feature concatenation, we also explore novel observation modelling schemes that allow for greater flexibility in combining the tandem and standard features at hidden Markov model (HMM) outputs

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Cross-lingual automatic speech recognition using tandem features

Automatic speech recognition requires many hours of transcribed speech recordings in order for an acoustic model to be effectively trained. However, recording speech corpora is time-consuming and expensive, so such quantities of data exist only for a handful of languages — there are many languages for which little or no data exist. Given that there are acoustic similarities between different languages, it may be fruitful to use data from a well-supported source language for the task of training a recogniser in a target language with little training data. Since most languages do not share a common phonetic inventory, we propose an indirect way of transferring information from a source language model to a target language model. Tandem features, in which class-posteriors from a separate classifier are decorrelated and appended to conventional acoustic features, are used to do that. They have the advantage that the language used to train the classifier, typically a Multilayer Perceptron (MLP) need not be the same as the target language being recognised. Consistent with prior work, positive results are achieved for monolingual systems in a number of different languages. Furthermore, improvements are also shown for the cross-lingual case, in which the tandem features were generated using a classifier not trained for the target language. We examine factors which may predict the relative improvements brought about by tandem features for a given source and target pair. We examine some cross-corpus normalization issues that naturally arise in multilingual speech recognition and validate our solution in terms of recognition accuracy and a mutual information measure. The tandem classifier in work up to this point in the thesis has been a phoneme classifier. Articulatory features (AFs), represented here as a multi-stream, discrete, multivalued labelling of speech, can be used as an alternative task. The motivation for this is that since AFs are a set of physically grounded categories that are not language-specific they may be more suitable for cross-lingual transfer. Then, using either phoneme or AF classification as our MLP task, we look at training the MLP using data from more than one language — again we hypothesise that AF tandem will resulting greater improvements in accuracy. We also examine performance where only limited amounts of target language data are available, and see how our various tandem systems perform under those conditions

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin

Regularized subspace Gaussian mixture models for cross-lingual speech recognition

Abstract—We investigate cross-lingual acoustic modelling for low resource languages using the subspace Gaussian mixture model (SGMM). We assume the presence of acoustic models trained on multiple source languages, and use the global subspace parameters from those models for improved modelling in a target language with limited amounts of transcribed speech. Experiments on the GlobalPhone corpus using Spanish, Portuguese, and Swedish as source languages and German as target language (with 1 hour and 5 hours of transcribed audio) show that multilingually trained SGMM shared parameters result in lower word error rates (WERs) than using those from a single source language. We also show that regularizing the estimation of the SGMM state vectors by penalizing their ℓ1-norm help to overcome numerical instabilities and lead to lower WER. I

Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition

This paper studies cross-lingual acoustic modelling in the context of subspace Gaussian mixture models (SGMMs). SGMMs factorize the acoustic model parameters into a set that is globally shared between all the states of a hidden Markov model (HMM) and another that is specific to the HMM states. We demonstrate that the SGMM global parameters are transferable between languages, particularly when the parameters are trained multilingually. As a result, acoustic models may be trained using limited amounts of transcribed audio by borrowing the SGMM global parameters from one or more source languages, and only training the state-specific parameters on the target language audio. Model regularization using ℓ1-norm penalty is shown to be particularly effective at avoiding overtraining and leading to lower word error rates. We investigate maximum a posteriori (MAP) adaptation of subspace parameters in order to reduce the mismatch between the SGMM global parameters of the source and target languages. In addition, monolingual and cross-lingual speaker adaptive training is used to reduce the model variance introduced by speakers. We have systematically evaluated these techniques by experiments on the GlobalPhone corpus