Assessing costa rican children speech recognition by humans and machines

Abstract

In recent years, an increasing number of studies on human-computer interaction is taking place, due to the pervasive speech interfaces implemented in systems such as cell phones, personal and home automation assistants. These studies include automatic speech recognition (ASR) and speech synthesis, and are considering a wider variety of conditions of the signals, such as noise and reverberation, and accents and age-related effects as well. For example, one of the key challenges is the development of ASR for children’s speech. Since the current systems have a dependency on language and accents, thus, to improve it, the investigations of speech recognition technologies suitable for children are needed. In this paper, we assess commercial ASR systems for the recognition of Costa Rican children’s speech, for users with ages ranging between three and fourteen years old. To establish a comparison and numeric validation of the ASR systems in recognizing children’s isolated words, we conducted a large subjective listening test that computes the differences and challenges that remains for the state-of-the art ASR systems. The results provide evident numeric differences between ASR systems and human perceptions, especially for younger children. Additionally, we provide suggestions for future research directions in the field.