A Low-Complexity Speech Codec Using Parametric Dithering for ASR

Dithering is a technique commonly used to improve the perceptual quality of lossy data compression. In this work, motivated by low-complexity wearable applications, we analytically and experimentally justify the use of dithering for Automatic Speech Recognition (ASR) input compression, evaluated on OpenAI's Whisper Model. We hypothesize a relationship between the mean squared error, the autocorrelation vector of the quantization error evaluated at a specific lag$(\tau=5)$, and ASR performance under lossy compression. Using this hypothesis, we propose a parametric dithering technique for a low-complexity speech compression pipeline. Specifically, we define two dithers indexed by$m$as:$f_{V_{m, \alpha}}(v)=\alpha \Lambda_{2 \Delta((\alpha-1) m+2-\alpha)}(v)+(1-\alpha) \delta(v), \quad m \in \{1,2\}$where$\Lambda_{2 a}(v) \triangleq \frac{1}{a^{2}}(a-\vert v\vert)$for$\vert v\vert \leq a$, with$\Delta$representing the quantization interval size and$\alpha$as the control parameter.