Modular neural networks exploit multiple front-ends to improve speech recognition systems

Integration of acoustic and articulatory information with application to speech recognition

“…One major advantage of the TIMIT task is the availability of the time aligned phonetic transcription which facilitates the AF MLPs training. Furthermore, the TIMIT corpus has been used by many other researchers in the speech processing community [2,4,10] and the performance of our system is comparable to other researchers [5]. While context-dependent phoneme models will give a better performance, our goal here is to demonstrate the usefulness of the fusion and context-independent phoneme model can simplify our experiments significantly.…”

“…While a single ASR system can perform quite well, many recent works are focusing on the integration of multiple systems [4,7,8,14,16,17] that result in a better performance than using a single system alone. Almost all of these focused on the integration during different stages of the recognition process, such as at frame level [7], state level [4,16,17] or word level [8]. For some system fusions, combination at the state level has been shown to be more effective [14].…”