Srishta Technology provides audio and speech annotation services for ASR, voice assistants, call analytics, speech emotion recognition, speaker diarization, multilingual datasets and conversational AI model training.
Transcribe
Speech to text
Segment
Speaker turns
Label
Intent & emotion
Deliver
Model-ready data
Speech datasets
Transcription, diarization, intent and entity labels
Voice AI ready
Prepared for ASR, IVR, assistant and call analytics
Human QA
Guidelines, reviewer checks and correction cycles
Flexible delivery
CSV, JSON, JSONL, SRT, VTT or custom format
What we annotate
Audio data becomes useful for AI only when it is labeled with clear context. Speech models need accurate transcripts, speaker turns, timestamps, intent, emotion, noise conditions, domain vocabulary and quality markers.
We help teams prepare audio datasets that are easier to train, evaluate and improve. The workflow can be designed for a small pilot batch, continuous annotation pipeline or a large multilingual data project.
Services
Choose a focused labeling task or combine transcription, speaker labeling, intent tagging and quality review into one annotation workflow.
Convert speech recordings into accurate written text with rules for pauses, filler words, timestamps, punctuation and speaker turns.
Identify and label different speakers in conversations, interviews, meetings, calls and multi-speaker audio files.
Label user intent, topics, entities and slots for voice assistants, call-routing systems, IVR automation and conversational AI.
Tag emotional cues, sentiment, tone, escalation signals and customer satisfaction markers in voice interactions.
Classify non-speech and mixed audio for sound recognition, safety systems, media analysis and environment detection.
Support speech models with pronunciation, phoneme, accent, mispronunciation and language-learning related annotations.
Poor annotation
Model-ready annotation
Annotation workflow
We structure the annotation workflow so each batch follows the same rules, review process and delivery format.
Define audio type, languages, domains, label taxonomy, transcription style and delivery format.
Organize files, metadata, instructions, privacy rules, sampling plan and annotation guidelines.
Label speech, speakers, timestamps, intents, emotions, topics or audio events as per project scope.
Run quality checks, reviewer validation, disagreement resolution and guideline correction.
Export annotations in CSV, JSON, JSONL, SRT, VTT or custom format with project documentation.
Use model feedback, edge cases and quality reports to refine guidelines and future batches.
Capabilities
Create clear project-specific annotation rules for transcription, speakers, intents, emotion and quality labels.
Add segment-level timestamps for speech turns, sound events, important phrases or QA review points.
Plan language-specific annotation workflows, reviewer checks and vocabulary rules for multilingual datasets.
Use sampling, reviewer checks, agreement review and correction loops to improve consistency.
Support redaction workflows, masking rules and secure handling for sensitive voice data where required.
Handle healthcare, finance, legal, telecom, education or enterprise terms with custom instructions.
Deliver annotations in structured schemas that can be used by training, evaluation or analytics teams.
Provide batch status, quality notes, issue logs and delivery summaries for project tracking.
Data types
Customer support calls, sales calls, IVR recordings and quality-monitoring samples.
Multi-speaker conversations with speaker turns, timestamps and summaries.
Short command phrases for assistants, devices, apps and automation flows.
Environmental sounds, alarms, music, noise, silence, activities and event-based audio.
Clinical notes, consultation recordings and healthcare-specific spoken content.
Speech samples across languages, accents, dialects and regional vocabulary.
Quality process
Speech data is sensitive to accents, noise, domain words, overlapping speech and ambiguous emotion. We use guidelines and review loops to reduce inconsistency.
Before labeling starts, we define what should be transcribed, ignored, timestamped, redacted and tagged. This avoids inconsistent labels later.
Annotators follow project examples, edge-case rules, language instructions and sample-reviewed tasks before working on large batches.
Quality review can include peer review, senior reviewer checks, random sampling, issue logs and correction cycles.
We monitor repeated mistakes, ambiguous labels, guideline gaps and annotator disagreement so the dataset improves across batches.
Use cases
Industries
Label customer calls for transcription, call reason, agent behavior, escalation, sentiment and quality monitoring.
Support medical voice notes, consultation recordings, healthcare support calls and domain-specific transcription workflows.
Annotate customer conversations, support calls, compliance audio, fraud signals and policy-related voice interactions.
Prepare speech datasets for assistants, bots, IVR, command recognition, wake-word workflows and conversational AI.
Label pronunciation, fluency, pauses, mistakes, language levels and spoken learning content for EdTech products.
Annotate podcasts, interviews, videos, sound events, music, background audio and multilingual media content.
Delivery formats
Simple tabular annotation delivery for transcription, labels, timestamps and metadata.
Structured delivery for training pipelines, nested labels, speaker turns and model evaluation.
Subtitle-style timestamped transcription for media, video, training and review workflows.
Project-specific format for internal ML pipelines, annotation tools or customer systems.
Project process
We review sample audio quality, languages, domains, speakers, file formats and expected labels.
We define transcription rules, speaker labels, timestamps, taxonomy, edge cases and delivery schema.
A small batch is annotated first to validate guidelines, output format and quality expectations.
Approved rules are used for larger batches with tracking, reviewer checks and issue logs.
Reviewers validate samples, fix errors, resolve ambiguous labels and update instructions where needed.
Final annotations are delivered with batch notes, quality observations and the agreed data format.
Annotation stack
Data readiness
We can structure annotation projects for model training, evaluation datasets, call analytics, voice assistant development, multilingual speech data and enterprise AI workflows.
Create labeled speech data for ASR, voice AI and conversational systems.
Prepare validation and benchmark sets with reviewed labels and consistent rules.
Label call reasons, sentiment, escalation, agent behavior and customer outcomes.
Use failed model outputs and edge cases to improve future data batches.
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Explore →Book a Discussion
Share the type of audio you need to annotate, who will use the data, and your AI training goals. We'll help you design an annotation workflow that delivers high-quality datasets for speech and audio models.
FAQ
From transcription to speaker labeling, intent tagging and emotion annotation, we can help structure your audio dataset for model training and analytics.