Evaluating Pre-trained Speech Encoders for Spontaneous Speech Detection and Out of Domain Synthetic Speech Generalisation in Indic Languages

Transformer-based models have shown strong accuracy in distinguishing spontaneous from scripted speech and natural from synthetic speech, but these results are established on a narrow set of well-resourced language benchmarks and have not been extended across Indic languages, nor has embedding geometry been used to explain encoder behaviour or deepfake generalisation failure. We address these gaps by evaluating five frozen transformer encoders, AST, Vaani-FastConformer, Wav2vec2, Whisper and BEATs, across 22 Indic languages, and by conducting a multi-system TTS generalisation experiment across four TTS models. Beyond accuracy, we present language isolation probing and centroid proximity analysis. Probing reveals an encoder-dependent trade-off between language-discriminability and spontaneity detection. Centroid analysis shows that out-of-domain generalisation is predicted by a training system's proximity to unseen TTS embeddings, not its distance from natural speech, a finding with direct implications for training data selection in real-world deepfake detectors.

SraVaani 1.0: Scaling Inclusive Speech Recognition for Indic Languages

India's linguistic landscape spans over 700 languages and thousands of dialects, yet the vast majority of automatic speech recognition (ASR) systems support only a small fraction of this diversity. We present SraVaani-1.0, a multilingual ASR model covering 65 Indian languages and dialects, many of which currently have no publicly available or competing ASR system. SraVaani-1.0 is built on a FastConformer architecture and trained from scratch through a three-stage pipeline.In the first stage, we perform self-supervised pretraining on 31,255 hours of unlabelled speech from the VAANI corpus using a contrastive learning objective. In the second stage, we introduce an audio-image representation alignment stage that leverages the paired images and speech available in the VAANI corpus. This multimodal alignment encourages the speech encoder to learn semantically richer representations by exploiting the relationship between visual context and spoken content, thereby improving downstream recognition, particularly for low-resource languages.In the final stage, the aligned encoder is fine-tuned end-to-end using a Hybrid Token-and-Duration Transducer (TDT)-CTC decoder on 31,263 hours of labelled multilingual Indian speech compiled from 24 public datasets spanning 65 languages and dialects. We evaluate SraVaani-1.0 against three state-of-the-art multilingual ASR systems across eight benchmarks. SraVaani-1.0 achieves the lowest word error rate (WER) on a large number of language-dataset pairs while remaining competitive with the best-performing systems on high-resource languages.Most importantly, it is the only open-source evaluated model that provides transcription capability for multiple low-resource and tribal Indian languages, which are assessed exclusively on the VAANI benchmark.

Vaani Benchmark V1.0: An Inclusive Multimodal Benchmark Dataset for Hindi

Benchmarking is critical for the systematic evaluation and comparison of automatic speech recognition (ASR) systems. While several open-source datasets are available for Hindi ASR, existing benchmarks remain limited in geographic diversity, demographic representation, and transcription robustness. We introduce an inclusive, multimodal Hindi ASR benchmark collected from 104 districts across India. The dataset consists of spontaneous speech elicited using image prompts and recorded in real-world acoustic conditions across diverse demographic groups. Each audio segment is annotated with three independent transcriptions, enabling multi-reference evaluation that accounts for permissible orthographic and lexical variations. This design supports more robust, inclusive, and realistic ASR evaluation. We benchmark multiple open-source and proprietary ASR models and report their comparative performance on the benchmark dataset.

Analyzing Language and Geographical Variation in Speech Representations Across 60 Indic Languages

Self-supervised speech encoders are often fine-tuned with language supervision, which can overlook geographical variation. To understand the learned representations under joint supervision of language and district compared to language-only supervision, we fine-tune Whisper-base and Wav2Vec2.0-base for classification tasks with joint language-district (386 classes) and language-only classification (60 languages). The language-district supervision improves district discrimination conditioned on language in the embedding space while strong marginal language classification. We analyze the structure of the learned embeddings using Normalized Conditional Mutual Information (NCMI), showing that language-district supervision produces global language clusters with structured within language subclusters aligned to district variation, enhancing geographical separability without degrading language-level organization.

A Comparative Study of Pre-trained Speech Encoders and Training Objectives for Large-Scale Indic Spoken Language Identification

Spoken language identification (LID) for Indian languages is a challenging problem due to the large number of languages, significant phonetic overlap among related varieties, and the scarcity of labeled data for many low-resource languages. In this work, we present a systematic comparative study of two pre-trained speech encoders -- Whisper and FastConformer -- combined with a linear classifier for large-scale Indic LID spanning 42 languages across four linguistic families. We evaluate both encoders in frozen (linear probing) and fine-tuned settings, and compare three training objectives: cross-entropy (CE), supervised contrastive loss with cross entropy (CE + supCon), and hierarchical softmax (HSM). Models are trained on the Vaani dataset and evaluated in a cross-corpus setting on Vaani-Test (held-out), FLEURS, and Kathbath, providing insights into domain generalization. The frozen FastConformer encoder achieves over 90\% macro accuracy on FLEURS and Kathbath without any task-specific adaptation, substantially outperforming Whisper on out-of-domain benchmarks, while fine-tuned Whisper yields stronger in-domain performance. HSM consistently outperforms CE and CE+SupCon for both encoders across all benchmarks, with the largest gains on out-of-domain test sets. CE+SupCon degrades FastConformer's cross-corpus generalization, suggesting that the contrastive objective over-specializes representations to in-domain conditions. Per-family analysis shows that Central Indo-Aryan varieties are the hardest to discriminate, with Hindi--Urdu and the Sadri--Chhattisgarhi--Surgujia cluster being the dominant confusion pairs.

Factors affecting ASR performance: A study using state of the art ASR models in Indic Languages

ASR performance varies across languages, speakers, and recording conditions, yet systematic analysis for Indic languages remain limited. We present a large-scale study of decoded outputs from multiple open-source ASR models evaluated on diverse Indian speech datasets in zero-shot settings. We analyze linguistic, speaker-level, and acoustic factors across Hindi, Bengali, Kannada, Telugu, and Marathi. We examine correlations between WER and speaker traits such as average word length, speaking rate, and utterance duration across multiple model dataset pairs. For Hindi, we further analyze audio factors including telephone codecs, bit depth, resampling, and background noise. Results reveal both cross lingual patterns and language-specific sensitivities, showing how speaker behavior and signal processing choices affect ASR robustness in real world Indic scenarios.

A study on the impact of region specific data on the performance of Indic ASR

Automatic Speech Recognition (ASR) systems are widely deployed across linguistically diverse regions, yet their ability to generalize across fine-grained geographic variation remains underexplored. We present a systematic study of cross-district ASR generalization for Indian languages, analyzing the impact of regional variation on performance. Using finetuning as a controlled probe, we train models on speech from a single district and evaluate them on other districts within the same language. We examine trends across multiple train test district pairs and quantify performance differences. To assess geographic effects, we analyze the correlation between WER and inter district distance using two distance measures. Our results show consistent correlations between geographic distance and WER, highlighting the challenges of regional generalization and the need for geographically diverse speech data in ASR development and evaluation in India.

VAANI: Capturing the language landscape for an inclusive digital India

Project VAANI is an initiative to create an India-representative multi-modal dataset that comprehensively maps India's linguistic diversity, starting with 165 districts across the country in its first two phases. Speech data is collected through a carefully structured process that uses image-based prompts to encourage spontaneous responses. Images are captured through a separate process that encompasses a broad range of topics, gathered from both within and across districts. The collected data undergoes a rigorous multi-stage quality evaluation, including both automated and manual checks to ensure highest possible standards in audio quality and transcription accuracy. Following this thorough validation, we have open-sourced around 289K images, approximately 31,270 hours of audio recordings, and around 2,067 hours of transcribed speech, encompassing 112 languages from 165 districts from 31 States and Union territories. Notably, significant of these languages are being represented for the first time in a dataset of this scale, making the VAANI project a groundbreaking effort in preserving and promoting linguistic inclusivity. This data can be instrumental in building inclusive speech models for India, and in advancing research and development across speech, image, and multimodal applications.