[Show abstract][Hide abstract] ABSTRACT: In this chapter, we present our recent advances in the formulation and development of an in-vehicle hands-free route navigation
system. The system is comprised of a multi-microphone array processing front-end, environmental sniffer (for noise analysis),
robust speech recognition system, and dialog manager and information servers. We also present our recently completed speech
corpus for in-vehicle interactive speech systems for route planning and navigation. The corpus consists of five domains which
include: digit strings, route navigation expressions, street and location sentences, phonetically balanced sentences, and
a route navigation dialog in a human Wizard-of-Oz like scenario. A total of 500 speakers were collected from across the United
States of America during a six month period from April-Sept. 2001. While previous attempts at in-vehicle speech systems have
generally focused on isolated command words to set radio frequencies, temperature control, etc., the CU-Move system is focused
on natural conversational interaction between the user and in-vehicle system. After presenting our proposed in-vehicle speech
system, we consider advances in multi-channel array processing, environmental noise sniffing and tracking, new and more robust
acoustic front-end representations and built-in speaker normalization for robust ASR, and our back-end dialog navigation information
retrieval sub-system connected to the WWW. Results are presented in each sub-section with a discussion at the end of the chapter.
[Show abstract][Hide abstract] ABSTRACT: Acoustic feature extraction from speech constitutes a fundamental component of automatic speech recognition (ASR) systems. In this paper, we propose a novel feature extraction algorithm, perceptual-MVDR (PMVDR), which computes cepstral coefficients from the speech signal. This new feature representation is shown to better model the speech spectrum compared to traditional feature extraction approaches. Experimental results for small (40-word digits) to medium (5k-word dictation) size vocabulary tasks show varying degree of consistent improvements across different experiments; however, the new front-end is most effective in noisy car environments. The PMVDR front-end uses the minimum variance distortionless response (MVDR) spectral estimator to represent the upper envelope of the speech signal. Unlike Mel frequency cepstral coefficients (MFCCs), the proposed front-end does not utilize a filterbank. The effectiveness of the PMVDR approach is demonstrated by comparing speech recognition accuracies with the traditional MFCC front-end and recently proposed PMCC front-end in both noise-free and real adverse environments. For speech recognition in noisy car environments, a 40-word vocabulary task, PMVDR front-end provides a 36% relative decrease in word error rate (WER) over the MFCC front-end. Under simulated speaker stress conditions, a 35-word vocabulary task, the PMVDR front-end yields a 27% relative decrease in the WER. For a noise-free dictation task, a 5k-word vocabulary task, again a relative 8% reduction in the WER is reported. Finally, a novel analysis technique is proposed to quantify noise robustness of an acoustic front-end. This analysis is conducted for the acoustic front-ends analyzed in the paper and results are presented.
Speech Communication 02/2008; · 1.55 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a maximum likelihood (ML) based frame selection approach. A fixed frame rate adopted in most state-of-the-art speech recognition systems can face some problems, such as accidentally meeting noisy frames, assigning the same importance to each frame, and pitch asynchronous representation. As an attempt to avoid those problems, our approach selects reliable frames from a fine resolution along the time axis in a phoneme recognition task, we show that significant improvements are achieved with the frame selection approach comparing to a system with a fixed frame rate
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on; 06/2006
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