Multi-band automatic speech recognition |
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| Affiliation: | 1. University of Science and Technology of China, Hefei, Anhui, China;2. iFlytek Co., Ltd., Hefei, Anhui, China;3. Northwestern Polytechnical University, Xian, Shaanxi, China;4. Georgia Institute of Technology, Atlanta, Georgia, USA;1. Agronomy College, Qingdao Agricultural University, Qingdao 266109, China;2. USDA-ARS, Center for Agricultural Resources Research, Fort Collins, CO 80526, USA;3. USDA-ARS, Rangeland Resources and Systems Research Unit, Fort Collins, CO 80526, USA;4. USDA-ARS, Rangeland Resources and Systems Research Unit, Cheyenne, WY 82009, USA;5. Texas A&M AgriLife Research, Texas A&M University, Temple, TX 77843, USA;6. Dept. Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA |
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| Abstract: | This paper presents a new architecture for automatic speech recognition systems which is characterized by the division of the spectral domain of the speech signal into several independent frequency bands. This model is based on the psycho-acoustic work of Fletcher (1953) who proposed a similar principle for the human auditory system. Jont B. Allen published a paper in 1994 in which he summarized the work of Fletcher and also proposed to adapt the multi-band paradigm to automatic speech recognition (ASR) (Allen, 1994). Many researchers have then studied this principle and built such ASR systems. The goal of this paper is to analyse some of the most important issues in the design of a multi-band ASR system in order to determine which architecture it should have in which environment. Two other major problems are then considered: how to train multi-band systems and how to use them for continuous ASR. |
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