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