A Spectrogram Model for Enhanced Source Localization and Noise-Robust ASR
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State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
Microphone arrays are useful in meeting rooms, where speech needs to be acquired and segmented. For example, automatic speech segmentation allows enhanced browsing experience, and facilitates automatic analysis of large amounts of data. Spontaneous multi-p ...
This paper proposes a simple, computationally efficient \mbox{2-mixture} model approach to discriminate between speech and background noise at the magnitude spectrogram level. It is directly derived from observations on real data, and can be used in a full ...
This paper proposes a simple, computationally efficient 2-mixture model approach to discriminate between speech and background noise at the magnitude spectrogram level. It is directly derived from observations on real data, and can be used in a fully unsup ...
Microphone arrays are useful in meeting rooms, where speech needs to be acquired and segmented. For example, automatic speech segmentation allows enhanced browsing experience, and facilitates automatic analysis of large amounts of data. Spontaneous multi-p ...
In this paper, we present a robust speech acquisition system to acquire continuous speech using a microphone array. A microphone array based speech recognition system is also presented to study the environmental interference due to reverberation, backgroun ...
Detection and localization of speakers with microphone arrays is a difficult task due to the wideband nature of speech signals, the large amount of overlaps between speakers in spontaneous conversations, and the presence of noise sources. Many existing aud ...
Speaker turn detection is an important task for many speech processing applications. However, accurate segmentation can be hard to achieve if there are multiple concurrent speakers (overlap), as is typically the case in multi-party conversations. In such c ...
Speaker turn detection is an important task for many speech processing applications. However, accurate segmentation can be hard to achieve if there are multiple concurrent speakers (overlap), as is typically the case in multi-party conversations. In such c ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
