Seungjong Lee, Taewook Kang, John Bell, M. Haghighat, Alberto J. Martinez, M. Flynn
{"title":"具有60 mel频率能量特征的8元频率选择声学波束形成器和比特流特征提取器，可实现95%的语音识别精度","authors":"Seungjong Lee, Taewook Kang, John Bell, M. Haghighat, Alberto J. Martinez, M. Flynn","doi":"10.1109/vlsicircuits18222.2020.9162783","DOIUrl":null,"url":null,"abstract":"A synergistic approach to beamforming and feature extraction, reduces processing complexity and die area, and delivers the high SNR required for reliable speech recognition. The 1.1mm2 IC combines frequency-selective bitstream beamforming, bitstream Mel frequency-band feature extraction, and an array of continuous-time sigma-delta modulators (SDMs) without area/power-intensive decimation. When coupled with a DNN, the prototype achieves 95.3% accuracy in recognizing spoken words from the Tensorflow dataset.","PeriodicalId":252787,"journal":{"name":"2020 IEEE Symposium on VLSI Circuits","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An 8-Element Frequency-Selective Acoustic Beamformer and Bitstream Feature Extractor with 60 Mel-Frequency Energy Features Enabling 95% Speech Recognition Accuracy\",\"authors\":\"Seungjong Lee, Taewook Kang, John Bell, M. Haghighat, Alberto J. Martinez, M. Flynn\",\"doi\":\"10.1109/vlsicircuits18222.2020.9162783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A synergistic approach to beamforming and feature extraction, reduces processing complexity and die area, and delivers the high SNR required for reliable speech recognition. The 1.1mm2 IC combines frequency-selective bitstream beamforming, bitstream Mel frequency-band feature extraction, and an array of continuous-time sigma-delta modulators (SDMs) without area/power-intensive decimation. When coupled with a DNN, the prototype achieves 95.3% accuracy in recognizing spoken words from the Tensorflow dataset.\",\"PeriodicalId\":252787,\"journal\":{\"name\":\"2020 IEEE Symposium on VLSI Circuits\",\"volume\":\"146 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Symposium on VLSI Circuits\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/vlsicircuits18222.2020.9162783\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/vlsicircuits18222.2020.9162783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An 8-Element Frequency-Selective Acoustic Beamformer and Bitstream Feature Extractor with 60 Mel-Frequency Energy Features Enabling 95% Speech Recognition Accuracy
A synergistic approach to beamforming and feature extraction, reduces processing complexity and die area, and delivers the high SNR required for reliable speech recognition. The 1.1mm2 IC combines frequency-selective bitstream beamforming, bitstream Mel frequency-band feature extraction, and an array of continuous-time sigma-delta modulators (SDMs) without area/power-intensive decimation. When coupled with a DNN, the prototype achieves 95.3% accuracy in recognizing spoken words from the Tensorflow dataset.
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