Speech recognition in noisy environments with the aid of microphone arrays

Compernolle, Dirk Van; Ma, Weiye; Diest, M Van

doi:10.21437/eurospeech.1989-167

Cited by 3 publications

(2 citation statements)

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“…The array structure used is that described in [5], where the adaptation of a two stage system (see figure 3) is switched with a speech/pause detector. The first stage is that involved with the beamforming of the array, and is readapted when speech is present.…”

Section: System Descriptionmentioning

confidence: 99%

“…The overall system proposed in this contribution looks for robustness at low computational and hardware joint cost. Instead of complex projection techniques in the cepstral space [3] or important modifications in the HMM architectures [4], we place an speech enhancement stage dealing with noisy reverberant speech [5] and a simple spectral normalization technique [6], to compensate for the effects introduced in the acoustic stage. Of course those effects will not (*) This work has been funded by CICYT under Project TIC94-0030 be completely cancelled, but this rather simple system will work far better than the mono channel original system.…”

Section: Introductionmentioning

confidence: 99%

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Increasing robustness in GMM speaker recognition systems for noisy and reverberant speech with low complexity microphone arrays

González-Rodríguez¹,

Ortega‐Garcia²,

Martín³

et al. 1996

4th International Conference on Spoken Language Processing (ICSLP 1996)

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In this paper we describe the additive robustness obtained through the combined use of a first acoustic processing step based on a low complexity microphone array, followed by a spectral normalization step. Microphone arrays have shown to provide good results in reducing different sources of acoustic degradation. However, microphone arrays produce linear filtering effects that need to be compensated in order to obtain a minimal spectral distortion. In this contribution we will present the combination of a microphone array together with different well known spectral normalization techniques as preprocessing stages to a Gaussian Mixture Models (GMM) based text-independent speaker recognition system. We will show that the combination of these extensively used techniques in the fields of speech enhancement and robust speaker recognition respectively, greatly improves the results obtained when the system is tested in noisy reverberant environments with short utterances from unconstrained conversational speech.

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Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increasing robustness in GMM speaker recognition systems for noisy and reverberant speech with low complexity microphone arrays

González-Rodríguez¹,

Ortega‐Garcia²,

Martín³

et al. 1996

4th International Conference on Spoken Language Processing (ICSLP 1996)

View full text Add to dashboard Cite

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Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Liang

Iwnicki

Ball

et al. 2015

Mechanical Systems and Signal Processing

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Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.

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