Matrix Quantization and LPC Vocoder Based Linear Predictive for Low-Resource Speech Recognition system

Abstract

Over the last ten years, there has been significant progress in the use of low-rate speech coders in voice applications for computers, military communications, and civil communications. This advancement has been made possible by the development of new speech coders that can generate high-quality speech at low data rates. The majority of existing coders include spectral representation of speech, speech waveform matching, and ”optimization” of the coder’s performance for human hearing. The goal of this paper is to provide a thorough evaluation of voice coding methods for educational purposes, with a particular emphasis on the algorithms used in low-rate cellular communication standards. The algorithm we developed using a voice-excited LPC vocoder produces clear, low-distortion results. Ordinary LPCs, on the other hand, fall short of vocoders because they can handle signals other than speech, such as music. To improve quality, additional bandwidth is used to reduce the bit rate. To improve the quality, we tried two approaches. The first was to increase the number of bits required to quantize the DCT coefficients. This coefficient would outperform the inverse DCT in closer error rearrangements. The second possibility is to increase the total number of quantized coefficients. As a result, error array rearrangements would be more accurate. The goal is to identify the point at which a method improvement outperforms the previous, better result. Other coding methods become more complex, but this vocoder suffices.