Name
Title | ||
|---|---|---|
Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures |
Abstract | ||
|---|---|---|
In this paper, we propose a formal methodology for tuning the parameters of a single-microphone speech enhancement system for hands-free devices. The tuning problem is formulated as a large-scale nonlinear programming problem that is solved by a genetic algorithm to determine the global solution. A conversational speech database is automatically generated by modeling the interactivity in telephone conversations, and perceptual objective quality measures are used as the optimization criteria for the automated tuning over the generated database. A subjective listening test is then performed by comparing the automatically tuned system based on objective criteria to the system tuned by expert human listeners. Subjective and objective evaluation result shows that the proposed automated tuning methodology greatly improves the enhanced speech quality, potentially saving resources over manual evaluation, speeding up development and deployment time, and guiding the algorithmic design. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/HSCMA.2014.6843252 | Hands-free Speech Communication and Microphone Arrays |
Keywords | Field | DocType |
audio databases,echo suppression,genetic algorithms,microphones,nonlinear programming,speech enhancement,enhanced speech quality,formal methodology,genetic algorithm,hands-free devices,large-scale nonlinear programming problem,perceptual objective measures,simulated conversational database,single-microphone speech enhancement system,speech database,speech enhancement algorithms,subjective listening test,telephone conversations,tuning methodology,tuning problem,acoustic echo cancellation,conversation analysis,perceptual objective quality,acoustics,databases,speech,tuning,noise | Speech enhancement,Interactivity,Speech processing,Software deployment,Algorithm design,Voice activity detection,Computer science,Nonlinear programming,Speech recognition,Genetic algorithm,Database | Conference |
Citations | PageRank | References |
2 | 0.38 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Giacobello | 1 | 117 | 12.33 |
| Jason Wung | 2 | 16 | 4.45 |
| Ramin Pichevar | 3 | 56 | 9.92 |
| Joshua Atkins | 4 | 7 | 4.20 |