Speech Recognition TRIZ Cases

Grammar-based labeling constructs structured schemas to generate labeled sentences, reducing manual effort and cost for dialog model training.

An audio front end suppresses false wakeword activations during playback using double-talk detection.

A self-trigger prevention system for voice devices using double-talk detection to suppress wakewords during playback.

Hierarchical grammar processing determines user goals from natural language inputs, reducing system creation cost while maintaining interaction flexibility.

A speech recognition apparatus updates personalized language models using user identification data and global model results to adapt to individual voice patterns.

A voice recognition module authenticates users via acoustic parameters to trigger smart contract execution.

A learning device maps images and speech into a shared latent space using neural network encoders with self-attention mechanisms.

Voiceprints enable avatar authentication across virtual environments, resolving interoperability limits and processing delays through universal templates.

A vehicle media player switches to low power mode by detecting motion and sound levels, reducing battery drain during stationary periods.

A signal processing system uses reference noise prototypes to enhance speech signals in noisy environments.

Segmenting audio buffers into noise and signal-plus-noise parts enables robust direction of arrival estimation by filtering spatially-coherent interference.

Speech recognition software identifies keyword occurrences in audio data by generating time-aligned phoneme sequences.

A spiking neural network segregates mixed audio signals into individual sources using temporal coincidence detection.

A sound event detecting module identifies repeating audio signals using feature vector similarity matrices.

Transmitting text transcripts alongside voice signals enables audio reconstruction of lost speech segments corrupted by wireless interference.

A server modulates audio signals to match target dialects during calls.

Segment-level parameter selection resolves transcription accuracy drops in multi-program broadcast streams.

A speech processing system dynamically adjusts grammar weights based on usage data to improve recognition accuracy.

A call analytics server segments audio into single-speaker units using Kullback-Leibler divergence for precise diarization.

Iterative neural network training refines spectral masks and removes harmonic distortion artifacts, resolving low fidelity in noisy single-track audio mixtures.

Computer method samples ASR test datasets based on user-defined attributes to generate tailored evaluation sets.

Processor selects keyword recognition models based on operating state to identify user voice inputs.

Dynamic filter bank frequency adjustment accounts for vocal tract length variations, improving speech recognition accuracy without fixed parameter limitations.

A data controller computing device creates a shared payment account linked to primary and secondary user payment accounts.

A speech activity analyzer processes touch screen pressure data to identify user contact patterns for reliable voice detection.

An analysis unit compares captured audio signals against predefined sequences to identify elementary contexts within a user's environment.

Automated translation device resolves communication barriers for hearing impaired individuals by replacing human interpreters with real-time text conversion.

A vibration actuator converts voice signals into tactile patterns for language learning.

A voice synthesis device generates intermediate voice quality by calculating characteristic parameter values between preset elements.

Server adapts speaker-independent keyword model to detect user-designated keywords across multiple users, resolving multi-user recognition limitations.

A speech-to-text matching unit collates edited text with original speech data using preserved time information.

A processor-implemented method applies frame-level normalization to neural network hidden layers using calculated statistical values.

A contextual speech recognition system constructs a limited command vocabulary graph before user selection to process audio inputs.

Skip-connected convolutional layers process voice frames to reduce signal distortion and noise without manual preset methods.

A two-stage audio processing system detects candidate activation phrases locally before transmitting minimal data to a client device for confirmation.

A wordpiece-phoneme model rescores acoustic features using a biasing finite-state transducer to map foreign terms into the recognition graph.

A voice and motion text input system places text objects in a virtual space for intuitive interaction.

A voice-controlled apparatus converts audio signals into text commands to generate dialogue-streams and workflow records for operator monitoring.

A hybrid phoneme-word lattice structure extracts terms from audio signals using contextual models and Viterbi decoding.

Dynamic encoder selection switches between close-talk and far-talk models, achieving matched-condition accuracy without runtime retraining overhead.

Recognition engine computes posterior probabilities to spot keywords in audio streams with reduced computational overhead.

Multi-stage error analysis categorizes speech recognition failures to resolve the contradiction between measurement precision and device complexity.

Head mounted display adjusts visual output using voice and scenery detection to resolve readability versus environmental awareness trade-offs.

A pronunciation error detection apparatus compares phoneme reliability between non-native and native speaker models to identify errors without correct sentences.

A voice interface application integrates with organizational systems to enable centralized device control and user permissions.

A speaker selecting device calculates long-time and short-time likelihoods of acoustic features to identify utterance speakers.

An efficient memory transformer reduces latency and computation in speech recognition models.