| Paper: | SP-P13.2 | ||
| Session: | General Topics in Robust Speech Recognition | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Robust Speech Recognition | ||
| Title: | A NEW VOICE ACTIVITY DETECTOR USING SUBBAND ORDER-STATISTICS FILTERS FOR ROBUST SPEECH RECOGNITION | ||
| Authors: | Javier Ramírez; Universidad de Granada | ||
| José C. Segura; Universidad de Granada | |||
| Carmen Benítez; Universidad de Granada | |||
| Ángel de la Torre; Universidad de Granada | |||
| Antonio J. Rubio; Universidad de Granada | |||
| Abstract: | Currently, there are technology barriers inhibiting speech processing systems working under extreme noisy conditions. The emerging applications of speech technology, especially in the fields of wireless communications, digital hearing aids or speech recognition, are some examples of such systems often requiring a noise reduction technique in combination with a precise voice activity detector (VAD). This paper presents a new VAD for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm uses long-term information about the speech signal to formulate the decision rule and estimates the subband SNR using specialized order statistics filters (OSFs). The proposed algorithm is compared to the most commonly used VADs in the field, in terms of speech/non-speech discrimination and also in terms of recognition performance when the VAD is used in an automatic speech recognition (ASR) system. Experimental results demonstrate a sustained advantage over different VAD methods including standard VADs such as G.729 and AMR which are used as a reference, the VADs of the Advanced Front-End (AFE) for distributed speech recognition (DSR), and recently reported algorithms. | ||
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