Speech coding at 4800 bps for mobile satellite communications

A speech compression project has recently been completed to develop a speech coding algorithm suitable for operation in a mobile satellite environment aimed at providing telephone quality natural speech at 4.8 kbps. The work has resulted in two alternative techniques which achieve reasonably good communications quality at 4.8 kbps while tolerating vehicle noise and rather severe channel impairments. The algorithms are embodied in a compact self-contained prototype consisting of two AT and T 32-bit floating-point DSP32 digital signal processors (DSP). A Motorola 68HC11 microcomputer chip serves as the board controller and interface handler. On a wirewrapped card, the prototype's circuit footprint amounts to only 200 sq cm, and consumes about 9 watts of power

DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508

Low Delay Sparse and Mixed Excitation CELP Coders for Wideband Speech Coding

Code Excited Linear Prediction (CELP) algorithmsare proposed for compression of speech in 8 kHz band atswitched or variable bit rate and algorithmic delay not exceeding2 msec. Two structures of Low-Delay CELP coders are analyzed:Low-delay sparse excitation and mixed excitation CELP. Sparseexcitation is based on MP-MLQ and multilayer models. Mixedexcitation CELP algorithm stems from the narrowband G.728standard. As opposed to G.728 LD-CELP coder, mixed excitationcodebook consists of pseudorandom vectors and sequencesobtained with Long-Term Prediction (LTP). Variable rate codingconsists in maximizing vector dimension while keeping therequired speech quality. Good speech quality (MOS=3.9according to PESQ algorithm) is obtained at average bit rate 33.5kbit/sec

Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.