Search

This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods between correct phonemes and other ones caused by hidden factors, the robustness of ASR systems can be improved. In this paper, we introduce a canonicalization method that is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to each hidden factor canonicalization, and a DPF selector which selects an optimum DPF vector as an input of the HMM-based classifier. The proposed method resolves gender factors and speaker variability, and eliminates noise factors by applying the canonicalzation based on the DPF extractors and two-stage Wiener filtering. In the experiment on AURORA-2J, the proposed method provides higher word accuracy under clean training and significant improvement of word accuracy in low signal-to-noise ratio (SNR) under multi-condition training compared to a standard ASR system with mel frequency ceptral coeffient (MFCC) parameters. Moreover, the proposed method requires a reduced, two-fifth, Gaussian mixture components and less memory to achieve accurate ASR.

Segmentation of speech into its corresponding phones has become very important issue in many speech processing areas such as speech recognition, speech analysis, speech synthesis, and speech database. In this paper, for accurate segmentation in speech recognition applications, we introduce Distinctive Phonetic Feature (DPF) based feature extraction using a twostage NN (Neural Networks) system consists of a RNN (Recurrent Neural Network) in the first stage and an MLN (Multi-Layer Neural Network) in the second stage. The RNN maps continuous acoustic features, Local Feature (LF), onto discrete DPF patterns, while the MLN constraints DPF context or dynamics in an utterance. The experiments are carried out using JNAS (Japanese Newspaper Article Sentences) continuous utterances that contains vowels and consonants. The proposed DPF based feature extractor provides good segmentation and high recognition rate with a reduced mixture-set of HMMs (Hidden Markov Models) by resolving co-articulation effect.

A pitch-synchronous (PS) auditory feature extraction method, based on ZCPA (zero-crossings peak-amplitudes), has been proposed (Ghulam, M. et al., Proc. ICSLP04, 2004) and was shown to be more robust than the conventional ZCPA (Kim, D.S. et al., IEEE Trans. Speech Audio Process., vol.7, no.1, p.55-69, 1999). We examine the effect of auditory masking, both simultaneous and temporal, in the PS-ZCPA method. We also observe the effect of varying the number of histogram bins on the way to find out the optimum parameters of the proposed method. Experimental results demonstrate the improved performance of the PS-ZCPA method achieved by embedding auditory masking into it; for example, with both the masking methods embedded, the performance increases to 73.71% from the 69.92% obtained without masking for PS-ZCPA, while it showed little improvement with an increased number of histogram bins.

In this paper, we propose a novel pitch-synchronous auditory-based feature extraction method for robust automatic speech recognition (ASR). A pitch-synchronous zero-crossing peak-amplitude (PS-ZCPA)-based feature extraction method was proposed previously [1,2], and showed improved performance except while modulation enhancement was integrated together with Wiener filter (WF)-based noise reduction and auditory masking into it [3]. However, since zero-crossing is not an auditory event, we propose a new pitch-synchronous peak-amplitude (PS-PA)-based method to make a feature extractor of ASR more auditory-like. We also examine the effect of WF-based noise reduction, modulation enhancement, and auditory masking into the proposed PS-PA method using Aurora-2J database. The experimental results showed the superiority of the proposed method over the PS-ZCPA method, and eliminated the problem due to the reconstruction of zero-crossings from modulated envelope. The highest relative performance over MFCC was achieved as 67.33% using the PS-PA method together with WF-based noise reduction, modulation enhancement, and auditory masking.

In this paper, we propose a novel feature extraction method based on an auditory nervous system for robust automatic speech recognition (ASR). In the proposed method, a pitchsynchronous mechanism is embedded in ZCPA (ZeroCrossings Peak-Amplitudes), which has previously been shown to outperform the conventional features in the presence of noise. A noise-robust non-delayed pitch determination algorithm (PDA) is also developed. In the experiment, the proposed pitch-synchronous ZCPA (PS-ZCPA) was proved more robust than the original ZCPA method. Moreover, a simple noise subtraction (NS) method is also integrated in the proposed method and the performance was evaluated using the Aurora-2J database. The experimental results showed the superiority of the proposed PS-ZCPA method with NS over the PS-ZCPA method without NS.

In this paper, we propose a novel confidence scoring method that is applied to N-best hypotheses output from an HMM-based classifier. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses and boundaries of all the monophones in the hypotheses. In the second pass, an SM(sub-space method)-based verifier tests the hypotheses by comparing confidence scores. We discuss how to convert a monophone similarity score of SM into a likelihood score, how to normalize the variations of acoustic quality in an utterance, and how to combine an HMM-based likelihood of word level and an SM-based likelihood of monophone level. In the experiments performed on speaker-independent word recognition, the proposed confidence scoring method significantly improves correct word recognition rate from 95.3% obtained by the standard HMM classifier to 98.0%.