Speech Synthesis TRIZ Cases

Substituting audio segments from secondary databases expands phonetic coverage for foreign words without requiring additional expensive recordings.

A voice output unit converts destination names into audible signals for operator confirmation.

Pitch variation training reduces the extensive audio data required for neural network speech generation.

Segmenting audio processing into full feature and energy-only phases reduces computational resource consumption while maintaining speech rhythm accuracy.

Extracting phase spectral data reduces database size while maintaining speech quality for memory-constrained devices.

A text-based speech synthesis method segments audio into diphones to match physical parameters with a database.

This interpolation engine combines weighted acoustic parameters from multiple voice fonts to adapt speech emotion and style while retaining base sound qualities.

Two-path search evaluates prosody consistency using statistical models to resolve pitch accent inaccuracies in concatenated speech synthesis.

A phase calculator derives spectral phase values from a target time-domain envelope to reconstruct processed audio signals with accurate amplitude.

A speech synthesis apparatus merges linguistic text data with visual feature vectors extracted from book images to generate natural-sounding audio output.

Segmenting cost functions into unit, concatenation, and event type components resolves computational complexity while improving prosody quality.

A system maps event characteristics to speech volume, pitch, and speed using a text-to-speech engine.

A text-to-speech method separates speaker voice from attributes using independent acoustic parameters.

A speech synthesis model generates audio using phoneme-level stress labels derived from annotated training data.

Segmenting synthesis and enhancement networks reduces quantization noise while maintaining computational efficiency.

Segmenting number strings into individual digits with unique inflections resolves mechanical IVR tone by applying segmentation and universality principles.

A text-to-speech system uses markup to integrate paralinguistic audio segments into synthesized speech streams.

A text-to-speech device converts acoustic model parameters using context sequences to generate voice signals.

Merges acoustic-prosodic models using dynamic accent weighting to maintain prosody continuity across multiple languages.

A modelability estimator detects temporal stationarity in speech signals to guide segment selection.

A training apparatus uses perception representation scores to train acoustic models for speech synthesis.

A text-to-speech system converts network addresses by segmenting usernames and domains to determine appropriate pronunciations.

A text-to-speech system selects phonemes and accents using a stochastic corpus to generate natural synthetic speech.

Band group delay compensation parameters correct phase spectrum deviations to improve speech waveform reproduction accuracy.

A cloud text-to-speech service generates voices through standardized APIs while hiding backend processing implementation from network clients.

A text-to-speech system uses emoticons to generate expressivity tags for natural speech synthesis.

Synthesizes speech signals with preset noise during registration to generate robust feature vectors for speaker recognition.

A speech processing system merges prerecorded audio segments with text-to-speech synthesis to generate hybrid output.

A speech synthesizer uses a neural network to model emotional cues from acoustic data.

Computer system selects best matching diphones and adjusts confidence scores to ensure smooth morphing during speech synthesis.

A voice synthesizing device transforms abstract user inputs into concrete acoustic parameters using a hierarchical score model.

Pitch constraints limit candidate units while offline cost tables lower computational complexity.

Extracting voice characteristics reduces storage space while maintaining speech naturalness in instant messaging systems.

Multi-scale spectrogram modeling predicts mel spectrograms from coarser to finer scales.

A parametric resynthesis method predicts acoustic parameters from degraded audio signals using a trained neural network.

A speech processing apparatus determines output time points using music progression data to synchronize audio playback.

A voice synthesis apparatus adjusts phonetic piece expansion rates to create aurally natural speech signals.

A text-to-speech system segments multilingual input for dedicated engines while displaying symbols visually.

Phase modulation of pulse signals at pitch marks embeds audio watermarking into synthesized speech without deteriorating sound quality.

Automated matching aligns user attributes with sound models and content attributes, resolving suboptimal synthesis by ensuring appropriate model selection.

A reduced script generates minimal speech assets to complete a concatenative text-to-speech voice.

Segmenting speech into harmonic and noise components via time-varying filters reduces computational complexity while maintaining high synthesis quality.

A fixed codebook searching apparatus generates a Toeplitz-type convolution matrix to perform efficient speech coding operations.

Incidental audio capture generates a voice dataset to synthesize speech that preserves sender identity and emotional expressiveness.

Mapping watermark patterns onto high-frequency sinewave signals enables real-time embedding in speech communication systems while reducing noise distortion.

Embedding spread-spectrum watermarks in synthesized speech prevents unauthorized modification and enables reliable authenticity verification.

A high-band generator creates a high frequency spectrum from narrowband input to reconstruct missing audio frequencies.

Hierarchical segmentation and dynamic programming resolve the contradiction between computational complexity and speech naturalness.

Segmented speech synthesis reduces terminal memory and computational load by transmitting only required audio indices.