Low Complexity On-Line Adaptation Techniques in Context of Assamese Spoken Query System
In this work, we present the development of an Assamese spoken query (SQ) system for accessing the price of agricultural commodities. The developed system intends to make the cultivators aware of the recent market trends. The SQ system enables the user to access the latest price of the commodity by calling the system using a landline/mobile phone. The spoken query input by the user is processed and then current price of the desired commodity in the given district is played back by the system. Features that make the system user friendly are incorporated into the design after taking feedbacks from local farmers. In other words, the system is tuned as per the needs of the users. Furthermore, the issues of adapting such query systems to the end user are also explored in this work. In case of the developed SQ system, the typical user responses are of extremely small duration (12seconds due to isolated word response from the user). Moreover, the employed adaptation approach must keep the system latency low since these systems are meant for real-time applications. Consequently, adapting such systems to the end user becomes an extremely challenging task. In this regard, acoustic model interpolation based adaptation techniques are proposed that employ interpolation weights derived in an approximate fashion. The proposed approaches try to minimize the latency in the system response by avoiding the iterative weight estimation procedure used in the earlier reported works. Even with extremely small amount of adaptation data, the proposed approaches are found to result in a relative improvement of 12% over the baseline ASR system.
on-line adaptation spoken query system automatic speech recognition assamese phone set fast adaptation sparse representation