This article presents a stand-alone automatic speech recognition system that accounts for listener movement, time-varying reverberation effects, environmental noise, and user position information beamforming approaches in an HRI setting. We raise the importance of replacing classical black-box integration technology applications with incorporation acoustic environment representation modeling, t...

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still be challenging to read due grammatical errors, disfluency, and other noises common spoken communication. These readable issues introduced by speakers ASR will impair the downstream tasks understanding human readers. In this work, we pres...

SU detection is specific to the context of automatic speech recognition (ASR) systems, which typically produce an unstructured sequence of words from an audio signal, and must then recover latent structural features in the signal such as word case (“true-casing”) and sentence boundaries (“SU detection”) in order for the output to be ready for human consumption. Recent efforts by the DARPA EARS ...

Several studies have shown that automatic speech recognition error rates are greater for children’s speech than for adult’s speech. Investigations have demonstrated that word recognition error rates increase as age decreases, and that recognition performance for children’s speech is more sensitive to bandwidth reduction, compared with adult speech. This paper presents the results of experiments...

Human beatboxing is a vocal art making use of speech organs to produce drum sounds and imitate musical instruments. Beatbox sound classification current challenge that can be used for automatic database annotation music-information retrieval. In this study, large-vocabulary human-beatbox recognition system was developed with an adaptation Kaldi toolbox, widely-used tool recognition. The corpus ...