Opportunities & Challenges In Automatic Speech Recognition
It identifies key problems for speech researchers and developers in enhancing ASR systems through parallel computing, but it is incremental as it builds on existing concepts without introducing new methods.
The paper explores how parallelism in computing can address challenges in automatic speech recognition, focusing on improving accuracy in noisy environments, increasing throughput for batch processing, and reducing latency for real-time applications, without presenting specific numerical results.
Automatic speech recognition enables a wide range of current and emerging applications such as automatic transcription, multimedia content analysis, and natural human-computer interfaces. This paper provides a glimpse of the opportunities and challenges that parallelism provides for automatic speech recognition and related application research from the point of view of speech researchers. The increasing parallelism in computing platforms opens three major possibilities for speech recognition systems: improving recognition accuracy in non-ideal, everyday noisy environments; increasing recognition throughput in batch processing of speech data; and reducing recognition latency in realtime usage scenarios. This paper describes technical challenges, approaches taken, and possible directions for future research to guide the design of efficient parallel software and hardware infrastructures.