India’s linguistic diversity encompasses multiple language families, including the Indo-Aryan and Dravidian, which represent distinct phonological and morphological characteristics. This study aims to evaluate and compare the performance of end-to-end automatic speech recognition (ASR) systems for three Indo-Aryan languages—Marathi, Odia, and Gujarati—and three Dravidian languages—Tamil, Telugu, and Malayalam. Using four transformer-based pre-trained models—Wav2Vec2.0-base, XLSR-53, W2V2-BERT, and Whisper small—the analysis explores their adaptability to these languages’ linguistic features, with word error rate (WER) and character error rate (CER) serving as evaluation metrics. Results indicate that W2V2-BERT and XLSR-53 outperform other models, achieving lower WER and CER, especially for Indo-Aryan languages. However, higher error rates for Dravidian languages highlight challenges such as complex phonology and agglutinative morphology. This work provides a comparative insight into the strengths and limitations of pre-trained ASR models across India’s diverse linguistic landscape and underscores the need for language-specific adaptations to improve ASR accuracy for underrepresented languages.

In recent years wavelet transform has been found to be an effective tool for time–frequency analysis. Wavelet transform has been used as feature extraction in speech recognition applications and it has proved to be an effective technique for unvoiced phoneme classification. In this paper a new filter structure using admissible wavelet packet is analyzed for English phoneme recognition. These filters have the benefit of having frequency bands spacing similar to the auditory Equivalent Rectangular Bandwidth (ERB) scale. Central frequencies of ERB scale are equally distributed along the frequency response of human cochlea. A new sets of features are derived using wavelet packet transform's multi-resolution capabilities and found to be better than conventional features for unvoiced phoneme problems. Some of the noises from NOISEX-92 database has been used for preparing the artificial noisy database to test the robustness of wavelet based features.