Hyperarticulation detection in repetitive voice queries using pairwise comparison for improved speech recognition

Ranjitha Gurunath Kulkarni, Ahmed El Kholy, Ziad Al Bawab, Noha Alon, Imed Zitouni, Umut Ozertem, Shuangyu Chang

Abstract: Automatic speech recognition systems can benefit from cues in user voice such as hyperarticulation. Traditional approaches typically attempt to define and detect an absolute state of hyperarticulation, which is very difficult, especially on short voice queries. We present a novel approach for hyperarticulation detection using pairwise comparisons and demonstrate its application in a real-world speech recognition system. Our approach uses delta features extracted from a pair of repetitive user utterances. Results show significant improvements in WER (word error rate) by using hyperarticulation information as a feature in a second pass N-best hypotheses rescoring setup.