In conversational speech recognition, recognizers are generally equipped with a keyword spotting capability to accommodate a variety of speaking styles. In addition, language model incorporation generally improves the recognition performance. In conversational speech keyword spotting, there are two types of errors, false alarm and false rejection. These two types of errors are not modeled in la...

Abstract We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword is commonly used in smart speakers to listen for wake words, control applications adapt unknown dynamics an online fashion. highlight benefit multiply-accumulate (MAC) array 2 which ordin...

Designing a customized KeyWord Spotting (KWS) Deep Neural Network (DNN) for tiny sensors is time-consuming process, demanding training new model on remote server with dataset of collected keywords. This paper investigates the effectiveness DNN-based KWS classifier that can be initialized on-device simply by recording few examples target commands. At runtime, computes distance between DNN output...

Abstract Personalized voice triggering is a key technology in assistants and serves as the first step for users to activate assistant. involves keyword spotting (KWS) speaker verification (SV). Conventional approaches this task include developing KWS SV systems separately. This paper proposes single system called multi-task deep cross-attention network (MTCANet) that simultaneously performs SV,...

Keyword spotting (KWS) plays a crucial role in human–machine interactions involving smart devices. In recent years, temporal convolutional networks (TCNs) have performed outstandingly with less computational complexity, comparison classical neural network (CNN) methods. However, it remains challenging to achieve trade-off between small-footprint model and high accuracy for the edge deployment o...

In this paper, we propose an innovative integrated approach to leverage available spoken content while detecting events in consumer-generated multimedia data (i.e., YouTube videos). Spoken content in consumer videos exhibits several challenges. For example, unlike Broadcast News, the spoken audio is typically not labeled. Also, the audio track in consumer videos tends to be noisy and the spoken...

This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a...

This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a...