Speech Projects

Advancing speech technologies for Indian languages

Speech Recognition

Advancing Telugu speech recognition technologies

Telugu ASR Dataset

Q1 2024 - Q4 2024

Building comprehensive speech recognition dataset for Telugu

Key Outcomes

  • 1000 hours of annotated speech
  • Multi-dialect coverage
  • Quality validation pipeline
  • Standardized metadata format

Future Plans

  • Expand to 5000 hours
  • Add regional variations
  • Create automated cleaning tools
  • Implement continuous validation

Key Metrics

Total Hours
1.0K
Dialects
5
Speakers
100.0K
Quality Score
4.5

Mobile ASR Models

Q2 2024 - Q1 2025

Developing efficient mobile-first speech recognition models

Key Outcomes

  • Sub-100MB model size
  • Real-time recognition
  • Offline capability
  • Multi-dialect support

Future Plans

  • Optimize for various devices
  • Reduce memory footprint
  • Improve battery efficiency
  • Add streaming capability

Key Metrics

Model Size
95MB
Accuracy
94%
Latency
200ms
Battery Impact
Low

Text-to-Speech

Creating natural and expressive speech synthesis

TTS Dataset Development

Q4 2024 - Q4 2025

Creating high-quality TTS training data

Key Outcomes

  • 500 hours of professional audio
  • Prosody annotations
  • Emotion labels
  • Multiple speaking styles

Future Plans

  • Add more voice varieties
  • Expand emotion coverage
  • Improve annotation quality
  • Create style transfer datasets

Key Metrics

Audio Hours
500
Speakers
50
Emotions
8
Styles
5

10,000 TTS Voices

Q3 2024 - Q2 2025

Scaling voice synthesis to thousands of unique voices

Key Outcomes

  • Voice generation pipeline
  • Quality assessment metrics
  • Voice search system
  • Style preservation

Future Plans

  • Improve voice quality
  • Add style control
  • Create voice marketplace
  • Implement voice mixing

Key Metrics

Voices Generated
2
Quality Score
4.2
Styles Per Voice
3
Generation Speed
2s

Voice Cloning

Q4 2024 - Q4 2025

Developing accurate voice cloning technology

Key Outcomes

  • Few-shot voice adaptation
  • Identity preservation
  • Real-time synthesis
  • Emotion transfer

Future Plans

  • Reduce required sample length
  • Improve naturalness
  • Add emotion control
  • Implement style mixing

Key Metrics

Sample Length
30s
Similarity Score
4.8
Adaptation Time
10s
Emotion Accuracy
92%

Speech Technology Research

Exploring novel approaches to speech processing

Non-Transformer Speech Models

Q2 2024 - Q2 2025

Developing alternative architectures for speech processing

Key Outcomes

  • Novel architecture designs
  • Performance benchmarks
  • Resource utilization metrics
  • Scaling characteristics

Future Plans

  • Scale to production
  • Optimize training
  • Create deployment tools
  • Document architecture

Key Metrics

Performance Gain
25%
Resource Reduction
40%
Training Speed
2x
Model Size
60% smaller

Speech Embeddings

Q3 2024 - Q1 2025

Creating accent and speaker embedding systems

Key Outcomes

  • Accent classification
  • Speaker identification
  • Embedding visualization
  • Cross-lingual mappings

Future Plans

  • Improve accent detection
  • Add more languages
  • Create accent map tool
  • Develop style transfer

Key Metrics

Accent Accuracy
95%
Speaker Accuracy
98%
Embedding Dim
256
Languages Covered
10