Presear builds production speech and audio AI systems — automatic speech recognition, text-to-speech, speaker diarisation, and audio classification — with enterprise-grade accuracy across languages.
Technical Depth
From transcription to synthesis to emotion understanding — here are the core techniques powering our audio AI systems.
Building end-to-end ASR systems — from fine-tuned Whisper and wav2vec2 models to domain-adapted CTC and attention-based encoder-decoder architectures — optimised for low word error rate in noisy, accented, and domain-specific speech. We handle telephony-quality audio, spontaneous speech, and technical vocabulary with custom language models.
Generating natural, expressive speech from text using neural TTS architectures — Tacotron2, FastSpeech2, VITS — with voice cloning capabilities and prosody control for brand-consistent synthetic voices. We fine-tune for Indian languages, regional accents, and domain-specific pronunciation to produce voices indistinguishable from native speakers.
Segmenting multi-speaker audio into per-speaker segments and identifying individual speakers using x-vector and ECAPA-TDNN embeddings. Our diarisation pipelines handle overlapping speech, varying channel conditions, and unknown speaker counts — producing labelled transcripts that attribute every utterance to the correct speaker in real time.
Extracting paralinguistic signals — emotional state, sentiment polarity, stress level, and engagement intensity — from speech prosody, energy, and spectral features using deep learning classifiers. Deployed in call centre analytics and customer experience monitoring to surface emotional signals that text analysis alone misses.
Classifying audio streams into event categories — machine anomalies, environmental sounds, keyword spotting, music genre, and scene classification — using CNN-based spectrogram classifiers and transformer-based audio models. We build low-latency always-on classifiers suitable for edge deployment in industrial IoT and security monitoring applications.
Removing background noise, reverberation, and channel distortion from degraded speech using deep learning spectral suppression models — RNNoise, FullSubNet, and custom architectures trained on domain-specific noise profiles. Essential preprocessing for downstream ASR, speaker ID, and voice analytics applications in real-world noisy environments.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
We audit your existing audio assets — call recordings, dictation files, broadcast archives — and assess coverage across target languages, accents, speaking styles, and acoustic conditions. Where gaps exist, we design data collection protocols, speaker diversity requirements, and annotation standards to build the training corpus needed for production accuracy.
Converting raw recordings into training-ready feature representations — silence trimming, normalisation, VAD segmentation, feature extraction (MFCCs, mel spectrograms, raw waveforms) — and augmenting with noise injection, speed perturbation, and room impulse response convolution to improve robustness to real-world acoustic conditions.
Fine-tuning or training acoustic models — Whisper, wav2vec2, Conformer, or ESPnet architectures — on domain-specific speech data. We apply parameter-efficient fine-tuning techniques to minimise data requirements, track training with WER/CER metrics across diverse test partitions, and validate against real deployment conditions before considering a model production-ready.
Integrating n-gram and neural language models tuned on domain text corpora — product names, medical terminology, legal vocabulary, financial instruments — to improve transcription accuracy for specialised vocabulary that acoustic models alone cannot handle reliably. Shallow fusion and deep fusion approaches are evaluated for each deployment context.
Packaging speech models for production — building streaming WebSocket APIs with WebRTC compatibility for real-time transcription, batch REST APIs for offline processing, and on-premise containerised deployments for data-sensitive environments. We build observability layers tracking latency, WER drift, and language distribution shifts over time.
Real-World Impact
Production audio AI deployments across industries — each delivering measurable accuracy, efficiency, and experience improvements.
Core Challenge
Call centres process millions of interactions monthly, but only 1–2% are manually reviewed for quality assurance. Critical compliance breaches, customer dissatisfaction signals, and agent coaching opportunities go undetected — while manual review creates backlog and inconsistent standards across teams and shifts.
Who Benefits
Banks, insurance companies, telecom operators, and BPO providers that need 100% call coverage for compliance monitoring, automated QA scoring, churn signal detection, and agent coaching — without expanding the QA headcount linearly with call volume.
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Clinicians spend up to 40% of their working hours on documentation — dictating notes, updating EHRs, and transcribing patient consultations. Manual transcription is expensive, creates documentation delays, and introduces errors in technical medical terminology that general-purpose ASR systems cannot handle reliably.
Who Benefits
Hospitals, specialist clinics, and health IT providers that need domain-adapted ASR for clinical dictation, SOAP note generation, and real-time transcription of patient consultations — integrated directly into existing EMR/EHR workflows.
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Factory floor operators wearing gloves, working in high-noise environments, and operating heavy machinery cannot safely interact with touchscreen HMI panels. Traditional voice command systems fail in industrial acoustic conditions — high ambient noise, machinery vibration, and domain-specific command vocabularies.
Who Benefits
Equipment manufacturers, automotive assembly plants, and industrial automation vendors that need hands-free, noise-robust voice control for machinery operation, quality data entry, and maintenance workflows — improving both safety and throughput.
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Retailers serving multilingual markets need voice assistants that handle code-switching, regional accents, and colloquial phrasing across multiple languages simultaneously. Standard ASR models degrade significantly on Indian languages, regional dialects, and mixed-language queries that are common in real customer interactions.
Who Benefits
Retail chains, e-commerce platforms, and consumer service companies that serve linguistically diverse customer bases — needing voice assistants that work for Hindi, Tamil, Bengali, and other Indian languages at the same quality level as English.
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Industry-standard frameworks, pre-trained models, and audio processing libraries — chosen for accuracy, speed, and production reliability.
Frequently Asked
Answers to the questions product leaders, data science teams, and compliance officers ask before starting a speech AI engagement with Presear Softwares.
Ask Our Speech AI TeamPartner with Presear Softwares to build speech and audio AI systems with enterprise-grade accuracy — domain-adapted, multilingual, and designed to deliver value from day one.