N-Gram and RNN-LM Language Model Integration for End-to-End Amazigh Speech Recognition

Abstract

This work investigates how different language modeling techniques affect the performance of an end-to-end automatic speech recognition (ASR) system for the Amazigh language. A (CNN-BiLSTM-CTC) model enhanced with an attention mechanism was used as the baseline. During decoding, two external language models were integrated using shallow fusion: a trigram N-gram model built with KenLM and a recurrent neural network language model (RNN-LM) trained on the same Tifdigit corpus. Four decoding methods were compared: greedy decoding; beam search; beam search with an N-gram language model; and beam search with a compact recurrent neural network language model. Experimental results on the Tifdigit dataset reveal a clear trade-off: the N-gram language model produces the best results compared to RNN-LM, with a phonetic error rate (PER) of 0.0268, representing a relative improvement of 4.0% over the greedy baseline model, and translates into an accuracy of 97.32%. This suggests that N-gram models can outperform neural approaches when reliable, limited data and lexical resources are available. The improved N-gram approach notably outperformed both simple beam search and the RNN neural language model. This improvement is due to higher-order context modeling, its optimized interpolation weights, and its adaptive lexical weighting tailored to the phonotactic structure of the Amazigh language.