Acoustic Transducers for Automatic Speech Recognition

自动语音识别的声学传感器本研究的目的是通过主动噪声消除来改变噪声环境中的自动语音识别。该方法集成到一个封装中：（1）一组基于麦克风振膜的高品质因数谐振的声学过滤麦克风，以及（2）硅芯片上的微型扬声器，以通过提高信噪比主动噪音消除。这项研究将导致在硅芯片上的微型扬声器，用于在麦克风旁边产生声音，以主动消除噪音，从而区分噪音和语音。 用于微型扬声器的一种方法将是使用弹性顺应聚合物层来形成类似于扬声器的振膜。本研究还将开发一种品质因数约为40的谐振麦克风，其谐振频率可以电调谐。一组本底噪声极低的谐振麦克风将在麦克风级呈现带通滤波信号，这将是这些特定频率下声功率的准确表示。更广泛的影响这项研究将极大地影响语音指挥系统（以及蜂窝电话）在地铁、机场、行驶中的汽车、战场、可调谐谐振麦克风和微型扬声器将在人机接口中得到广泛的应用。拟议的活动还将包括：（1）将研究成果纳入声学微机电系统实验室课程;（2）来自代表性不足群体的学生参与研究。

Acoustic Transducers for Automatic Speech RecognitionObjectiveThe research objective is to transform automatic speech recognition in noisy environments by active noise cancellation. The approach is integrate into one package (1) a bank of acoustically filtered microphones that are based on a high quality-factor resonance of a microphone diaphragm and (2) a microspeaker on a silicon chip to improve the signal-to-noise ratio through active noise cancellation. Intellectual MeritThis research will lead to a microspeaker on a silicon chip for sound generation next to a microphone to actively cancel noise, and thus to discriminate noise from speech. One approach for the microspeaker will be to use an elastically compliant polymer layer to form a cantilever-like diaphragm. This research will also develop a resonant microphone with quality factor of about 40 with resonant frequencies that can electrically be tuned. A bank of the resonant microphones with extremely low noise floor will render bandpass-filtered signals at the microphone level, which will be accurate representations of acoustic powers at those specific frequencies.Broader ImpactThe research will greatly impact voice command systems (as well as cellular phones) in subways, airports, running automobiles, battlefields, etc. The tunable resonant microphone and the microspeaker will find broad applications to human-machine interfaces. The proposed activity will also include (1) incorporation of the research results into a lab course on acoustic microelectromechanical systems and (2) research involvement of students from underrepresented groups.