Evaluating Extended Long Short-Term Memory (xLSTM) Architectures for Sequential Modeling in Hindi Speech-to-Speech Pipelines

Sequential modeling of speech signals presents unique challenges arising from the need to capture long-range temporal dependencies across thousands of audio frames. Stan- dard Long Short-Term Memory (LSTM) networks, while effec- tive for short-context tasks, exhibit gradient degradation over ex- tended sequences, limiting their applicability in large-vocabulary, morphologically rich languages such as Hindi. Transformer- based architectures, though powerful, impose quadratic memory complexity with respect to sequence length, rendering them computationally prohibitive for resource-constrained inference environments. This paper presents an implementation and eval- uation of the Extended Long Short-Term Memory (xLSTM) architecture within a complete Hindi Speech-to-Speech (STS) pipeline. We detail the mathematical underpinnings of xLSTM’s exponential gating mechanisms and matrix memory structures, demonstrating their superiority in modeling long-term temporal dependencies. The pipeline encompasses raw audio ingestion, forced phoneme alignment formulated as a dynamic program- ming problem over time-series state spaces, aesthetic quality scoring of output segments, and inference optimization on Apple Silicon unified memory hardware. Results suggest that xLSTM provides compelling advantages for sequential audio modeling tasks with broader implications for financial and scientific time- series forecasting.