Artificial Intelligence & Machine Learning

Expert-Level Automatic Speech Recognition Test

Dive into sophisticated algorithms and models powering voice-to-text conversion in machine learning.

#CTC loss#ASR systems#speaker adaptation#end-to-end models#WER metrics#language modeling#MFCC features#beam decoding#neural acoustics

Total Questions

Difficulty Level

hard

Progress

0/0

Quiz Questions

Answer all questions below and test your knowledge.

1
What does CTC stand for in the context of end-to-end speech recognition models?
Question 1
2
In traditional ASR, what is the role of the HMM in hybrid DNN-HMM systems?
Question 2
3
Which feature extraction technique emphasizes human auditory perception in speech processing?
Question 3
4
During beam search decoding, what criterion selects the top hypotheses?
Question 4
5
What represents the blank symbol in CTC loss computation?
Question 5
6
What is the primary purpose of a pronunciation lexicon in ASR pipelines?
Question 6
7
Which adaptation method performs linear transformation in feature space for new speakers?
Question 7
8
In attention-based seq2seq ASR, what loss is commonly optimized for character-level output?
Question 8
9
What does Word Error Rate (WER) measure in ASR evaluation?
Question 9