Related Experiment Video
Updated: Aug 29, 2025
05:48
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
Published on: August 9, 2024
1.6K
A Deep Learning Based Approach to Synthesize Intelligible Speech with Limited Temporal Envelope Information
Summary
This study shows deep learning can synthesize intelligible speech using limited temporal envelope information. Even with fewer features, the synthesized speech achieved high objective and human intelligibility scores.
Area of Science:
- Speech Processing
- Artificial Intelligence
- Signal Processing
Background:
- Envelope waveforms from speech signals contain crucial intelligibility information for human communication.
- Temporal envelope information across frequency bands is vital for speech perception.
Purpose of the Study:
- To investigate the efficacy of a deep learning model in synthesizing intelligible speech using temporal envelope features.
- To evaluate the impact of reducing the number of temporal envelope features on synthesized speech intelligibility.
Main Methods:
- Utilized a deep learning-based model to synthesize speech.
- Extracted and processed temporal envelope information from speech signals.
- Reduced the number of temporal envelope features from 8 to 2 for analysis.
Main Results:
- The synthesized speech achieved high objective intelligibility scores, with an average Short-Time Objective Intelligibility (STOI) of 0.8.
- Human listening tests demonstrated excellent intelligibility, with an average word correct rate exceeding 97.5% across listeners.
Conclusions:
- Deep learning models can effectively synthesize highly intelligible speech even with limited temporal envelope information.
- The proposed system demonstrates potential for future applications in speech synthesis requiring reduced feature sets.
Related Concept Videos
01:07Elaborative Rehearsals
122
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
The effectiveness of...
122
01:21Linear Approximation in Time Domain
120
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
120
01:26Linear Approximation in Frequency Domain
128
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
128
01:11Sampling Continuous Time Signal
336
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
336
01:22Amplifying Signals via Enzymatic Cascade
8.7K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
8.7K
01:22Chunking and Rehearsal in Sensory Memory
279
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
279