Speech processing using digital MEMS microphones

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance

Digital Microphone Array - Design, Implementation and Speech Recognition Experiments

The instrumented meeting room of the future will help meetings to be more efficient and productive. One of the basic components of the instrumented meeting room is the speech recording device, in most cases a microphone array. The two basic requirements for this microphone array are portability and cost-efficiency, neither of which are provided by current commercially available arrays. This will change in the near future thanks to the availability of new digital MEMS microphones. This dissertation reports on the first successful implementation of a digital MEMS microphone array. This digital MEMS microphone array was designed, implemented, tested and evaluated and successfully compared with an existing analogue microphone array using a state-of-the-art ASR system and adaptation algorithms. The newly built digital MEMS microphone array compares well with the analogue microphone array on the basis of the word error rate achieved in an automated speech recognition system and is highly portable and economical