In this paper, we propose a new acoustic modeling technique called the Phone-Cluster Adaptive Training. In this approach, the parameters of context-dependent states are obtained by the linear interpolation of several monophone cluster models, which are themselves obtained by adaptation using linear transformation of a canonical Gaussian Mixture Model (GMM). This approach is inspired from the Cluster Adaptive Training (CAT) for speaker adaptation and the Subspace Gaussian Mixture Model (SGMM). The parameters of the model are updated in an adaptive training framework. The interpolation vectors implicitly capture the phonetic context information. The proposed approach shows substantial improvement over the Continuous Density Hidden Markov Model (CDHMM) and a similar performance to that of the SGMM, while using significantly fewer parameters than both the CDHMM and the SGMM. © 2013 IEEE.

Srinivasan Umesh

Department of Electrical Engineering

Acoustic model

Adaptive training

CLUSTER ADAPTIVE TRAINING

Context information

CONTINUOUS DENSITY HIDDEN MARKOV MODELS

GAUSSIAN MIXTURE MODEL

LINEAR INTERPOLATION

SUBSPACE GAUSSIAN MIXTURE MODELS

Communication channels (information theory)

Object Recognition

Telephone sets

Vectors

Speech Recognition

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Acoustic modeling using transform-based phone-cluster adaptive training

2013 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2013 - Proceedings

Recent studies have shown that in the case of under-resourced languages, use of articulatory features (AF) emerging from an articulatory model results in improved automatic speech recognition (ASR) compared to conventional mel frequency cepstral coefficient (MFCC) features. Articulatory features are more robust to noise and pronunciation variability compared to conventional acoustic features. To extract articulatory features, one method is to take conventional acoustic features like MFCC and build an articulatory classifier that would output articulatory features (known as pseudo-AF). However, these classifiers require a mapping from phone to different articulatory labels (AL) (e.g., place of articulation and manner of articulation), which is not readily available for many of the under-resourced languages. In this article, we have proposed an automated technique to generate phone-to-articulatory label (phone-to-AL) mapping for a new target language based on the knowledge of phone-to-AL mapping of a well-resourced language. The proposed mapping technique is based on the center-phone capturing property of interpolation vectors emerging from the recently proposed phone cluster adaptive training (Phone-CAT) method. Phone-CAT is an acoustic modeling technique that belongs to the broad category of canonical state models (CSM) that includes subspace Gaussian mixture model (SGMM). In Phone-CAT, the interpolation vector belonging to a particular context-dependent state has maximum weight for the center-phone in case of monophone clusters or by the AL of the center-phone in case of AL clusters. These relationships from the various context-dependent states are used to generate a phone-to-AL mapping. The Phone-CAT technique makes use of all the speech data belonging to a particular context-dependent state. Therefore, multiple segments of speech are used to generate the mapping, which makes it more robust to noise and other variations. In this study, we have obtained a phone-to-AL mapping for three under-resourced Indian languages namely Assamese, Hindi and Tamil based on the phone-to-AL mapping available for English. With the generated mappings, articulatory features are extracted for these languages using varying amounts of data in order to build an articulatory classifier. Experiments were also performed in a cross-lingual scenario assuming a small training data set (≈ 2 h) from each of the Indian languages with articulatory classifiers built using a lot of training data (≈ 22 h) from other languages including English (Switchboard task). Interestingly, cross-lingual performance is comparable to that of an articulatory classifier built with large amounts of native training data. Using articulatory features, more than 30% relative improvement was observed over the conventional MFCC features for all the three languages in a DNN framework. © 2016

Speech Communication

An automated technique to generate phone-to-articulatory label mapping

Phone-cluster adaptive training (Phone-CAT) is a subspace based acoustic modeling technique inspired from cluster adaptive training (CAT) and subspace Gaussian mixture model (SGMM). This paper explores three extensions, viz., increasing phonetic subspace dimension, including sub-states and speaker subspace, to the basic Phone-CAT model to improve its recognition performance. The latter two extensions are similar in implementation as that of SGMM as both acoustic models share a similar subspace framework. But, since the phonetic subspace dimension of Phone-CAT is constrained to be equal to the number of monophones, the first extension is not straightforward to implement. We propose a Two-stage Phone-CAT model where we increase the phonetic subspace dimension to that of the number of monophone states. This model will still be able to retain the center phone capturing property of the state-specific vectors in basic Phone-CAT. Experiments done on 33-hour train subset of Switchboard database shows improvements in recognition performance of basic Phone-CAT model with the inclusion of the proposed extensions. © 2016 IEEE.

2016 International Conference on Signal Processing and Communications, SPCOM 2016

Improved phone-cluster adaptive training acoustic model

Digital Signal Processing

Speaker Verification Using Adapted Gaussian Mixture Models

Journal	2013 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2013 - Proceedings
Open Access	No