Robust Speech Recognition with Small Microphone Arrays using the Missing Data Approach

Traditional microphone array speech recognition systems simply recognise the enhanced output of the array. As the level of signal enhancement depends on the number of microphones, such systems do not achieve acceptable speech recognition performance for arrays having only a few microphones. For small microphone arrays, we instead propose using the enhanced output to estimate a reliability mask, which is then used in missing data speech recognition. In missing data speech recognition, the decoded sequence depends on the reliability of each input feature. This reliability is usually based on the signal to noise ratio in each frequency band. In this paper, we use the energy difference between the noisy input and the enhanced output of a small microphone array to determine the frequency band reliability. Recognition experiments with a small array demonstrate the effectiveness of the technique, compared to both traditional microphone array enhancement and a baseline missing data system

Real-time implementation of a dual microphone beamformer : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Systems at Massey University, Albany, New Zealand

The main objective of this project is to develop a microphone array system, which captures the speech signal for a speech related application. This system should allow the user to move freely and acquire the speech from adverse acoustic environments. The most important problem when the distance between the speaker and the microphone increases is that often the quality of the speech signal is degraded by background noise and reverberation. As a result, the speech related applications fails to perform well under these circumstances. This unwanted noise components present in the acquired signal have to be removed in order to improve the performance of these applications. This thesis contains the development of a dual microphone beamformer in a Digital Signal Processor (DSP). The development kit used in this project is the Texas Instruments TMS320C6711 DSP Starter Kit (DSK). The switched Griffiths-Jim beamformer was selected as the algorithm to be implemented in the DSK. Van Compernolle developed this algorithm in 1990 by modifying the Griffiths-Jim beamformer structure. This beamformer algorithm is used to improve the quality of the desired speech signal by reducing the background noise. This algorithm requires atleast two input channels to obtain the spatial characteristics of the acquired signal. Therefore, the PCM3003 audio daughter card is used to access the two microphone signals. The software implementation of the switched Griffiths-Jim beamformer algorithm has two main stages. The first stage is to identify the presence of speech in the acquired signal. A simple Voice Activity Detector (VAD) based on the energy of the acquired signal is used to distinguish between the wanted speech signal and the unwanted noise signals. The second stage is the adaptive beamformer, which uses the results obtained from the VAD algorithm to reduce the background noise. The adaptive beamformer consists of two adaptive filters based on the Normalised Least Mean Squares (NLMS) algorithm. The first filter behaves like a beam-steering filter and it's only updated during the presence of speech and noise signal. The second filter behaves like an Adaptive Noise Canceller (ANC) and it is only updated when a noise alone period is present. The VAD algorithm controls the updating process of these NLMS filters and only one of these filters is updated at any given time. This algorithm was successfully implemented in the chosen DSK using the Code Composer Studio (CCS) software. This implementation is tested in real-time using a speech recognition system. This system is programmed in Visual Basic software using the Microsoft Speech SDK components. This dual microphone system allows the user to move around freely and acquire the desired speech signal. The results show a reasonable amount of enhancement in the output signal, and a significant improvement in the ease of using the speech recognition system is achieved