✦ LIBER ✦

Near-field Adaptive Beamformer for Robust Speech Recognition

✍ Scribed by Iain A. McCowan; Darren C. Moore; S. Sridharan

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 237 KB
Volume: 12
Category: Article
ISSN: 1051-2004
DOI: 10.1006/dspr.2001.0414

No coin nor oath required. For personal study only.

✦ Synopsis

This paper investigates a new microphone array processing technique specifically for the purpose of speech enhancement and recognition. The main objective of the proposed technique is to improve the low frequency directivity of a conventional adaptive beamformer, as low frequency performance is critical in speech processing applications. The proposed technique, termed near-field adaptive beamforming (NFAB), is implemented using the standard generalized sidelobe canceler (GSC) system structure, where a near-field superdirective (NFSD) beamformer is used as the fixed upper-path beamformer to improve the low frequency performance. In addition, to minimize signal leakage into the adaptive noise canceling path for near-field sources, a compensation unit is introduced prior to the blocking matrix. The advantage of the technique is verified by comparing the directivity patterns with those of conventional filter-sum, NFSD, and GSC systems. In speech enhancement and recognition experiments, the proposed technique outperforms the standard techniques for a near-field source in adverse noise conditions.  2002 Elsevier Science (USA)

📜 SIMILAR VOLUMES

Noise adaptation algorithms for robust s

Noise adaptation algorithms for robust speech recognition

✍ H.M. Cung; Y. Normandin 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 781 KB

Robust speech recognition for car enviro

Robust speech recognition for car environment noise

✍ Hiroaki Kokubo; Akio Amano; Nobuo Hataoka 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 92 KB

Integrated bias removal techniques for r

Integrated bias removal techniques for robust speech recognition

✍ Craig Lawrence; Mazin Rahim 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 522 KB

In this paper, we present a family of maximum likelihood (ML) techniques that aim at reducing an acoustic mismatch between the training and testing conditions of hidden Markov model (HMM)-based automatic speech recognition (ASR) systems. Our study is conducted in two phases. In the first phase, we e

Speaker adaptation techniques for speech

Speaker adaptation techniques for speech recognition using probabilistic models

✍ Koichi Shinoda 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 353 KB

N-Best-based unsupervised speaker adapta

N-Best-based unsupervised speaker adaptation for speech recognition

✍ Tomoko Matsui; Sadaoki Furui 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 251 KB

This paper proposes an instantaneous speaker adaptation method that uses N-best decoding for continuous mixture-density hidden-Markovmodel-based speech-recognition systems. This method is effective even for speakers whose decoding using speaker-independent (SI) models are error-prone and for whom sp

Adaptive Fusion of Speech and Lip Inform

Adaptive Fusion of Speech and Lip Information for Robust Speaker Identification

✍ T Wark; S Sridharan 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 269 KB

This paper compares techniques for asynchronous fusion of speech and lip information for robust speaker identification. In any fusion system, the ultimate challenge is to determine the optimal way to combine all information sources under varying conditions. We propose a new method for estimating con