SpeechYOLO: Detection and Localization of Speech Objects
| Autor: | Joseph Keshet, Tzeviya Sylvia Fuchs, Yael Segal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Computer Science - Machine Learning Computer science Speech recognition SIGNAL (programming language) Machine Learning (stat.ML) Function (mathematics) Convolutional neural network Object detection Computer Science - Sound Domain (software engineering) Machine Learning (cs.LG) Audio and Speech Processing (eess.AS) Statistics - Machine Learning Keyword spotting FOS: Electrical engineering electronic engineering information engineering Spontaneous speech Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | INTERSPEECH |
| Popis: | In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we present SpeechYOLO, which is inspired by the YOLO algorithm for object detection in images. The goal of SpeechYOLO is to localize boundaries of utterances within the input signal, and to correctly classify them. Our system is composed of a convolutional neural network, with a simple least-mean-squares loss function. We evaluated the system on several keyword spotting tasks, that include corpora of read speech and spontaneous speech. Our system compares favorably with other algorithms trained for both localization and classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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