{Preeti Rao Hitesh Tulsiani}

Abstract
This paper describes the system developed at I.I.T. Bombay for Query-by-Example Search on Speech Task (QUESST) within the MediaEval 2015 evaluation framework. Our system preprocesses the data to remove noise and performs subsequence DTW on posterior/bottleneck features obtained using four phone recognition systems to detect the queries. Scores from each of these subsystems are fused to get the single score per query-utterance pair which is then calibrated with respect to the cross entropy evaluation metric.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| keyword-spotting-on-quesst | IIT-B (eval) | ATWV: 0.0254 Cnxe: 0.9536 MTWV: 0.0421 MinCnxe: 0.9364 |
| keyword-spotting-on-quesst | IIT-B (dev) | ATWV: 0.0812 Cnxe: 0.9213 MTWV: 0.816 MinCnxe: 0.9082 |
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.