HMM-Based Phoneme Speech Recognition for Industrial Robot Control

A speech recognition system designed for controlling industrial robots via phoneme-based commands. The system, developed by Adwait Naik of K J Somaiya Medical College, uses Linear Predictive Coding and comprises a microphone array, voice module, and a 3-DOF robotic arm. It was validated through experiments involving simple and complex sentences for tasks like cube manipulation and pick-and-place.

Use Cases

• Training phoneme-based speech recognition models based on the described Linear Predictive Coding technique.
• Developing voice command systems for industrial robot manipulation based on the described three-segment system.
• Benchmarking speech recognition performance for robotics pick-and-place tasks based on the described test scenarios.
• Analyzing the robustness of phoneme recognition in noisy industrial environments based on the described microphone array setup.

Strengths

• The system architecture is concretely described, including a microphone array, voice module, and 3-DOF robotic arm.
• Validation involved specific robotics activities like manipulating a cube and pick-and-place tasks.
• The paper presents all test results from conducted experiments.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.

Provenance

Source

Adwait Naik, K J Somaiya Medical College

Collection Method

Research project involving a constructed system and experimental validation.