Robust Speech Recognition and Feature Extraction Using HMM2

This paper presents the theoretical basis and preliminary experimental results of a new HMM model, referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated through secondary, state specific, HMMs working in the acoustic feature space. Thus, while the primary HMM is performing the usual time warping and integration, the secondary HMMs are responsible for extracting/modeling the possible feature dependencies, while performing frequency warping and integration. Such a model has several potential advantages, such as a more flexible modeling of the time/frequency structure of the speech signal. When working with spectral features, such a system can also perform nonlinear spectral warping, effectively implementing a form of nonlinear vocal tract normalization. Furthermore, it will be shown that HMM2 can be used to extract noise robust features, supposed to correspond to formant regions, which can be used as extra features for traditional HMM recognizers to improve their performance. These issues are evaluated in the present paper, and different experimental results are reported on the Numbers95 database

Speech Recognition Using Advanced HMM2 Features

HMM2 is a particular hidden Markov model where state emission probabilities of the temporal (primary) HMM are modeled through (secondary) state-dependent frequency-based HMMs [12]. As shown in [13], a secondary HMM can also be used to extract robust ASR features. Here, we further investigate this novel approach towards using a full HMM2 as feature extractor, working in the spectral domain, and extracting robust formant-like features for standard ASR system. HMM2 performs a nonlinear, state-dependent frequency warping, and it is shown that the resulting frequency segmentation actually contains particularly discriminant features. To further improve the HMM2 system, we complement the initial spectral energy vectors with frequency information. Finally, adding temporal information to the HMM2 feature vector yields further improvements. These conclusions are experimentally validated on the Numbers95 database, where word error rates of 15\%, using only a 4-dimensional feature vector (3 formant-like parameters and one time index) were obtained

Increasing Speech Recognition Noise Robustness with HMM2

The purpose of this paper is to investigate the behavior of HMM2 models for the recognition of noisy speech. It has previously been shown that HMM2 is able to model dynamically important structural information inherent in the speech signal, often corresponding to formant positions/tracks. As formant regions are known to be robust in adverse conditions, HMM2 seems particularly promising for improving speech recognition robustness. Here, we review different variants of the HMM2 approach with respect to their application to noise-robust automatic speech recognition. It is shown that HMM2 has the potential to tackle the problem of mismatch between training and testing conditions, and that a multi-stream combination of (already noise-robust) cepstral features and formant-like features (extracted by HMM2) improves the noise robustness of a state-of-the-art automatic speech recognition system

HMM2- Extraction of Formant Features and their Use for Robust ASR

As recently introduced, an HMM2 can be considered as a particular case of an HMM mixture in which the HMM emission probabilities (usually estimated through Gaussian mixtures or an artificial neural network) are modeled by state-dependent, feature-based HMM (referred to as frequency HMM). A general EM training algorithm for such a structure has already been developed. Although there are numerous motivations for using such a structure, and many possible ways to exploit it, this paper will mainly focus on one particular instantiation of HMM2 in which the frequency HMM will be used to extract formant structure information, which will then be used as additional acoustic features in a standard Automatic Speech Recognition (ASR) system. While the fact that this architecture is able to automatically extract meaningful formant information is interesting by itself, empirical results will also show the robustness of these features to noise, and their potential to enhance state-of-the-art noise-robust HMM-based ASR

Evaluation of Formant-Like Features for ASR

This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been shown to be discriminant features for ASR. Combinations of automatically extracted formant-like features and `conventional', noise-robust, state-of-the-art features (such as MFCCs including spectral subtraction and cepstral mean subtraction) have previously been shown to be more robust in adverse conditions than state-of-the-art features alone. However, it is not clear how these automatically extracted formant-like features behave in comparison with true formants. The purpose of this paper is to investigate two methods to automatically extract formant-like features, and to compare these features to hand-labeled formant tracks as well as to standard MFCCs in terms of their performance on a vowel classification task