In this paper, an improved method of reducing ambient noise in speech signals is introduced. The proposed noise canceller was developed using a computationally efficient (DFT) filter bank to decompose input signals into sub-bands. The filter bank was based on a prototype filter optimized for minimum output distortion. A variable step-size version of the (LMS) filter was used to reduce the noise in individual branches. The subband noise canceller was aimed to overcome problems associated with the use of the conventional least mean square (LMS) adaptive algorithm in noise cancellation setups. Mean square error convergence was used as a measure of performance under white and ambient interferences. Compared to conventional as well as recently developed techniques, fast initial convergence and better noise cancellation performances were obtained under actual speech and ambient noise.
Robustness is a key issue in speech recognition. A speech recognition algorithm for Malay digits from zero to nine and an algorithm for noise cancellation by using recursive least squares (RLS) is proposed in this article. This system consisted of speech processing inclusive of digit margin and recognition using zero crossing and energy calculations. Mel-frequency cepstral coefficient vectors were used to provide an estimate of the vocal tract filter. Meanwhile dynamic time warping was used to detect the nearest recorded voice with appropriate global constraint. The global constraint was used to set a valid search region because the variation of the speech rate of the speaker was considered to be limited in a reasonable range which meant that it could prune the unreasonable search space. The algorithm was tested on speech samples that were recorded as part of a Malay corpus. The results showed that the algorithm managed to recognize almost 80.5% of the Malay digits for all recorded words. The addition of a RLS noise canceller in the preprocessing stage increased the accuracy to 94.1%.