Abstract
Speech signal processing for voice communications can be characterised in terms of silence compression, noise reduction, and speech compression. The limit in the channel bandwidth of voice communication systems requires efficient compression of speech and silence signals while retaining the voice quality. Silence compression by means of both voice activity detection (VAD) and comfort noise generation could present transparent speech-quality while substantially lowering the transmission bit-rate, since pause regions between talk spurts do not include any voice information. Thus, this thesis proposes smoothed likelihood ratio-based VAD, designed on the basis of a behavioural analysis and improvement of a statistical model-based voice activity detector. Input speech could exhibit noisy signals, which could make the voice communication fatiguing and less intelligible. This task can be alleviated by noise reduction as a preprocessor for speech coding. Noise characteristics in speech enhancement are adapted typically during the pause regions classified by a voice activity detector. However, VAD errors could lead to over- or under- estimation of the noise statistics. Thus, this thesis proposes mixed decision-based noise adaptation based on a integration of soft and hard decision-based methods, defined with the speech presence uncertainty and VAD result, respectively. At low bit-rate speech coding, the sinusoidal model has been widely applied because of its good nature exploiting the phase redundancy of speech signals. Its performance, however, can be severely smeared by mis-estimation of the pitch. Thus, this thesis proposes a robust pitch estimation technique based on the autocorrelation of spectral amplitudes. Another important parameter in sinusoidal speech coding is the spectral magnitude of the LP-residual signal. It is, however, not easy to directly quantise the magnitudes because the dimensions of the spectral vectors are variable from frame to frame depending on the pitch. To alleviate this problem, this thesis proposes mel-scale-based dimension conversion, which converts the spectral vectors to a fixed dimension based on mel-scale warping. A predictive coding scheme is also employed in order to exploit the inter-frame redundancy between the spectral vectors. Experimental results show that each proposed technique is suitable for enhancing speech quality for voice communications. Furthermore, an improved speech coder incorporating the proposed techniques is developed. The vocoder gives speech quality comparable to TIA/EIA IS-127 for noisy speech whilst operating at lower than half the bit-rate of the reference coder. Key words: voice activity detection, speech enhancement, pitch, spectral magnitude quantisation, low bit-rate coding.