Voice technologies are becoming foundational to digital inclusion in India, shaping how millions access public services, information, and the digital economy. Against this backdrop, a Developers’ Toolkit on voice technologies was launched at the India AI Summit Expo 2026 on February 20, 2026 to set out a practice framework supporting open, inclusive, and responsible voice technologies. The Developers’ Toolkit was jointly developed by ARTPARK @IISc, Digital Futures Lab and Trilegal with support from Digital India BHASHINI Division and the FAIR Forward - AI for All initiative, implemented by GIZ (German Development Cooperation) funded by the German Federal Ministry for Economic Cooperation and Development (BMZ), bringing together research, technical expertise, and ecosystem collaboration to advance responsible speech technologies in India. Executive Summary of the Report The development of speech and language technologies in the Indian context is constrained not by a lack of innovation, but by persistent structural gaps in data representation, quality assurance, evaluation practices, and governance. Models trained on narrow or homogenised datasets risk underperforming for large segments of the population, while post-hoc ethical safeguards and deployment fixes are insufficient to address foundational exclusions embedded early in the development lifecycle. The toolkit sets out a layered, lifecycle-oriented approach to building inclusive and robust speech artificial intelligence (AI) systems. It brings together strategies for diverse and representative data collection, linguistically informed model training, rigorous quality control, and deployment optimisation under real-world constraints, alongside embedded Responsible AI (RAI) practices. Ensuring Diverse Representation: Develop diversity wishlist: Leverage existing datasets and create a diversity wishlist based on demography, geography and linguistic nuances. Heterogeneous data collection: Adopt a variety of data collection methods like crowdsourcing, field-based initiatives, and community media platforms, documenting different forms of speech through read, extempore and role play scenarios. Apply linguistic expertise: To handle nuances like hybridism, codeswitching, coarticulation variability, and morphological complexities, invite Indic language experts for inputs at the data collection stage. Use synthetic data : Where feasible, use synthetic data to supplement gaps in data collection. Model training for linguistic nuances: Account for linguistic nuances through pre-training and fine-tuning on code-switched databases and regular evaluation. Layered data strategy: Use generic or foundational datasets and fine-tune with use-case specific datasets. Enhancing Data Quality and Building Inclusive Applications: Implement quality control mechanisms: Use rigorous quality control processes, including metadata verification (e.g., age, gender via video or WhatsApp calls), content checks (e.g., rejecting lowquality recordings based on error categories), and transcription accuracy assessments. Use detailed transcription guidelines: Adopt two-level transcription guidelines: level 1 for verbatim transcription and level 2 for standardised transcription with tags for errors and linguistic features. Specialised tools for transcription: Use specialised tools like Karya for data collection and Shoonya for transcription to ensure efficiency and scalability. Maintain Datacards: They represent a paradigm shift toward responsible AI development, serving as comprehensive metadata documents that detail dataset creation, composition, and limitations. Better benchmarks for evaluation: The disparities in datasets risk producing exclusionary outcomes, mainly when models are evaluated using a limited set of metrics like Word Error Rate (WER). Complementary metrics, such as answer error rate or intent accuracy, are necessary to reflect real-world usage better. Managing accent and pronunciation variations: To generalise across accent and pronunciation variations, incorporate diverse speech datasets capturing regional differences. Regular fine-tuning on region-specific data and adversarial training to reduce accent bias improves generalisation. Voice cloning and accent correction techniques can be incorporated to tackle this challenge while building the solution. Managing noisy speech in datasets: Noisy speech data, common in Indian settings, can be addressed by evaluating signal quality like Signal-to-Noise Ratio (SNR) and rejecting low-quality speech data. Employ noise-robust modelling techniques, such as synthetic noise augmentation or denoising autoencoders, as used in IndicWav2Vec and Indic Conformer. Model cards: Complementing datacards, model cards serve as structured documents that provide essential context and transparency for trained AI models. Model Deployment and Optimisation Strategies: Optimised offline models and hybrid offline–online approaches enable reliable operation under limited connectivity by balancing local inference with cloud support when available. Speech-to-speech models further reduce end-to-end latency by removing intermediate processing steps. Embedding Responsible AI practices: Meaningful engagement with language communities: Data compilation process for speech and language technology must begin with meaningful engagement with associated language communities. This involves understanding their needs and aspirations regarding language technology and ensuring they have a say in how their data is used. Documentation and packaging: Comprehensive documentation and standardised packaging including detailed guides on accessing, using, and contributing to the data and clear instructions for model deployment and fine-tuning. Ethical and Legal Pre-design: The consent mechanism, personally identifiable information (PII) reduction protocols, parameters for RAI as applicable to the specific use case, and data storage boundaries must be designed upfront, not as an afterthought, to ensure the flywheel operates at scale and remains compliant with privacy laws and ownership boundaries. Obtain informed consent: Where applicable, ensure that clear and unambiguous consent with an affirmative action is obtained from all participants, clearly explaining how their data will be used, stored, and shared. Protect privacy: Implement robust privacy protections, ensuring compliance with data protection laws like the Digital Personal Data Protection Act, 2023 (DPDP Act) or General Data Protection Regulation (GDPR) for international collaborations. Anonymise all personal data where possible to limit the applicability of the DPDP Act or other data protection regimes globally. Privacy Enhancing Technologies (PETs) such as analysing voice patterns on the fly without storing PII, should be actively explored and supported by the ecosystem Compliance with Copyright laws: Under Indian law, particularly the Copyright Act, 1957, multiple layers of intellectual property protection may apply: to underlying text or transcripts (as literary works), to voice recordings (as sound recordings), and to curated metadata, provided each meets originality requirements. The use of any copyrighted information requires a license from the person who owns the copyright. Ensure transparency: Be transparent about data collection purposes, share data management practices with participants and stakeholders. Foster accountability: Establish mechanisms for accountability, such as regular audits and clear governance structures. Identifying the license under which open sourcing is carried out: Licensing datasets and models is a critical decision that dictates their permissible use, modification, and distribution. Terms of use: Adopt Terms of Use and Acceptable Use Policies that define how the data may be accessed, shared, modified, and redistributed. These policies should explicitly address privacy, consent, attribution, commercial use, and downstream redistribution to ensure legal compliance and ethical usage. Selecting a hosting platform: The selection of a hosting platform for open source datasets and models should be guided by accessibility, scalability, version control, and community engagement features. Audit Techniques and Benchmarks: Audit techniques and robust benchmarks evaluate system performance against specified safety and fairness criteria throughout the deployment lifecycle. For Indic languages, this would involve ongoing evaluation against diverse accent and dialect benchmarks to ensure equitable performance. Mitigating Misuse: Regular and rigorous assessment of the AI system’s safety properties, including its robustness to adversarial attacks and its resilience in unforeseen circumstances. This is particularly important for voice systems susceptible to audio manipulations or voice impersonation attacks. Appropriate mitigation mechanisms, such as not collecting PII, using appropriate licensing, and watermarks should be considered for open datasets and models. To read the full report, click here.