Robust speech detection method for telephone speech recognition system

Kuroiwa, Shingo; Naito, Masaki; Yamamoto, Seiichi; Higuchi, Norio

doi:10.1016/s0167-6393(98)00072-7

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…This feature reported high noiserobustness [14]. The second feature is derived from the temporal sequence of the height and width information by using linear regression [13].…”

Section: A Audio-visual Integration For Vadmentioning

confidence: 99%

Two-layered audio-visual speech recognition for robots in noisy environments

Yoshida

Nakadai

Okuno

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Audio-visual (AV) integration is one of the key ideas to improve perception in noisy real-world environments. This paper describes automatic speech recognition (ASR) to improve human-robot interaction based on AV integration. We developed AV-integrated ASR, which has two AV integration layers, that is, voice activity detection (VAD) and ASR. However, the system has three difficulties: 1) VAD and ASR have been separately studied although these processes are mutually dependent, 2) VAD and ASR assumed that high resolution images are available although this assumption never holds in the real world, and 3) an optimal weight between audio and visual stream was fixed while their reliabilities change according to environmental changes. To solve these problems, we propose a new VAD algorithm taking ASR characteristics into account, and a linear-regression-based optimal weight estimation method. We evaluate the algorithm for auditoryand/or visually-contaminated data. Preliminary results show that the robustness of VAD improved even when the resolution of the images is low, and the AVSR using estimated stream weight shows the effectiveness of AV integration.

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“…This feature reported high noiserobustness [14]. The second feature is derived from the temporal sequence of the height and width information by using linear regression [13].…”

Section: A Audio-visual Integration For Vadmentioning

confidence: 99%

Two-layered audio-visual speech recognition for robots in noisy environments

Yoshida

Nakadai

Okuno

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…This solution increases the space size a lot and makes the search very slow. Following the idea proposed in (Kuroiwa, 1999), we analyzed the noise distribution throughout the utterance. We studied the training set and we found that 67.3% of the noises appear at the beginning of the utterance, 26.2% at the end and only the 6.5% appear between letters.…”

Section: Language Modelsmentioning

confidence: 99%

Spanish recognizer of continuously spelled names over the telephone

San-Segundo

Colás

Córdoba

et al. 2002

Speech Communication

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“…Davis [26] designed a state machine-based hangover scheme to improve the SDR. Sohn [14] used the hidden Markov model (HMM) to cover the trivial speeches, and Kuroiwa [27] designed a grammatical system to enhance the robustness of the VAD.…”

Section: Introductionmentioning

confidence: 99%

An efficient voice activity detection algorithm by combining statistical model and energy detection

Zhang

2011

EURASIP J. Adv. Signal Process.

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In this article, we present a new voice activity detection (VAD) algorithm that is based on statistical models and empirical rule-based energy detection algorithm. Specifically, it needs two steps to separate speech segments from background noise. For the first step, the VAD detects possible speech endpoints efficiently using the empirical rulebased energy detection algorithm. However, the possible endpoints are not accurate enough when the signal-tonoise ratio is low. Therefore, for the second step, we propose a new gaussian mixture model-based multipleobservation log likelihood ratio algorithm to align the endpoints to their optimal positions. Several experiments are conducted to evaluate the proposed VAD on both accuracy and efficiency. The results show that it could achieve better performance than the six referenced VADs in various noise scenarios.

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Robust speech detection method for telephone speech recognition system

Cited by 12 publications

References 16 publications

Two-layered audio-visual speech recognition for robots in noisy environments

Two-layered audio-visual speech recognition for robots in noisy environments

Spanish recognizer of continuously spelled names over the telephone

An efficient voice activity detection algorithm by combining statistical model and energy detection

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