Podcast
Questions and Answers
In the context of speech signals, what information do amplitude values primarily represent?
In the context of speech signals, what information do amplitude values primarily represent?
- The noise levels present in the signal.
- The intensity or loudness of the sound over time. (correct)
- The phonetic variations in the speech.
- The pitch and tone of the speech.
Which of the following is true about stationary signals, as opposed to non-stationary signals like speech?
Which of the following is true about stationary signals, as opposed to non-stationary signals like speech?
- Stationary signals contain multiple frequencies
- Stationary signals are non-changing in frequency over time (correct)
- Stationary signals' frequency changes over time
- Speech signals are considered stationary
What is a spectrogram primarily used for in speech analysis?
What is a spectrogram primarily used for in speech analysis?
- To convert speech to text directly.
- To isolate and remove noise from a speech signal.
- To represent the amplitude of a speech signal over time.
- To visualize the frequency components of a speech signal over time. (correct)
What is the main purpose of applying the Fourier Transform to speech analysis?
What is the main purpose of applying the Fourier Transform to speech analysis?
In the process of converting an analog audio signal to a digital format, what does the 'sampling rate' determine?
In the process of converting an analog audio signal to a digital format, what does the 'sampling rate' determine?
What does the term 'bit depth' refer to in digital audio?
What does the term 'bit depth' refer to in digital audio?
What is the standard number of MFCC coefficients typically used as features in speech recognition tasks?
What is the standard number of MFCC coefficients typically used as features in speech recognition tasks?
If a speech recognition system models phones at the level of individual frames and classifies each frame independently, what is one potential issue that might arise?
If a speech recognition system models phones at the level of individual frames and classifies each frame independently, what is one potential issue that might arise?
What is a key limitation of isolated word recognition systems?
What is a key limitation of isolated word recognition systems?
In the context of speech recognition, what is the primary challenge that Dynamic Time Warping (DTW) addresses?
In the context of speech recognition, what is the primary challenge that Dynamic Time Warping (DTW) addresses?
When calculating the edit distance between two words, what is the significance of the 'minimum edit distance'?
When calculating the edit distance between two words, what is the significance of the 'minimum edit distance'?
How does Dynamic Programming improve the efficiency of calculating Minimum Edit Distance?
How does Dynamic Programming improve the efficiency of calculating Minimum Edit Distance?
What is the role of the 'search trellis' in the context of Dynamic Time Warping (DTW)?
What is the role of the 'search trellis' in the context of Dynamic Time Warping (DTW)?
In Dynamic Time Warping (DTW), what is the purpose of allowing multiple input frames to align to the same template frame?
In Dynamic Time Warping (DTW), what is the purpose of allowing multiple input frames to align to the same template frame?
In Dynamic Time Warping (DTW), what does it mean to 'skip' a template frame?
In Dynamic Time Warping (DTW), what does it mean to 'skip' a template frame?
What is the significance of normalizing the DTW distance?
What is the significance of normalizing the DTW distance?
In the context of DTW, how is the cost typically calculated between two frames?
In the context of DTW, how is the cost typically calculated between two frames?
What is the primary advantage of using DTW with average templates?
What is the primary advantage of using DTW with average templates?
When aligning multiple templates of variable lengths using DTW, what is the purpose of 'master template'?
When aligning multiple templates of variable lengths using DTW, what is the purpose of 'master template'?
What is the benefit of averaging the features within each segment?
What is the benefit of averaging the features within each segment?
How accurate is the classification of each frame independently in the speech recognition system?
How accurate is the classification of each frame independently in the speech recognition system?
What is the meaning of Dynamic programming?
What is the meaning of Dynamic programming?
What do stationary signals mean?
What do stationary signals mean?
In Dynamic Time Warping, what happens when the input is so fast?
In Dynamic Time Warping, what happens when the input is so fast?
In Edit distance, what does the edit do?
In Edit distance, what does the edit do?
How are the transitions in Dynamic Time Warping (DTW)?
How are the transitions in Dynamic Time Warping (DTW)?
Which are the main limitations of ‘template model’?
Which are the main limitations of ‘template model’?
What happens if you know the exact segmentation of phonemes?
What happens if you know the exact segmentation of phonemes?
How is a new word calculated?
How is a new word calculated?
How is the tree ‘trellis’ in Dynamic Time Warping?
How is the tree ‘trellis’ in Dynamic Time Warping?
How is edit program’s distance calculation?
How is edit program’s distance calculation?
What is one of the most important functions of Dynamic Time Warping?
What is one of the most important functions of Dynamic Time Warping?
In what form can MFCCs be expanded?
In what form can MFCCs be expanded?
What is 'speech signal made of'?
What is 'speech signal made of'?
In spectrogram, what function does the short-time Fourier transform serve?
In spectrogram, what function does the short-time Fourier transform serve?
To have a constant metric, what should be done for 'normalizing to values'?
To have a constant metric, what should be done for 'normalizing to values'?
Isolated words can be classified directly?
Isolated words can be classified directly?
Why is Neveshtenin’s distance called minimum?
Why is Neveshtenin’s distance called minimum?
Flashcards
Speech Waveform
Speech Waveform
A sequence of quantized amplitude values representing loudness over time.
Fourier Transform
Fourier Transform
Decomposing a signal into its constituent frequencies.
Spectrogram
Spectrogram
A visual representation of the frequencies of a signal as it varies with time.
MFCCs
MFCCs
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Edit Distance
Edit Distance
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Minimum Edit Distance Algorithm
Minimum Edit Distance Algorithm
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Dynamic Programming
Dynamic Programming
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Search Trellis
Search Trellis
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Dynamic Time Warping
Dynamic Time Warping
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Normalization
Normalization
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Study Notes
- Dynamic Time Warping (DTW) is a method for comparing sequential features, especially in speech processing
Review of Speech Signals
- A speech signal consists of audio data, often stored as '.wav' files.
- The signal’s amplitude represents loudness over time.
- Digital audio is discrete and requires sampling.
- Sampling Rate is the Number of samples per second
- Common sample rate 16000 samples
- Bit depth determines precision
- Precision: 8-bit results in Amplitude Range 0 to 255 (unsigned)
- Precision: 16-bit results in Amplitude Range -32,768 to 32,767
- Precision: 24-bit results in Amplitude Range -8,388,608 to 8,388,607
- Precision: 32-bit results in Amplitude Range -1.0 to 1.0
- Libraries like Librosa normalize audio to a 32-bit float
Speech Waveforms and Frequency Analysis
- Automatic Speech Recognition (ASR) transcribes speech to text
- Speech waveforms in the time domain show amplitude over time.
- Waveform intensity represents loudness over time
- Frequency analysis, using Fourier Transform, identifies the frequencies present
- A Spectrogram visualizes frequency changes over time
- The Spectrogram shows frequency content over time
- Spectrogram's y-axis is Frequency in kilo Hertz
- Spectrogram's measure of relative intensity is dBFS: Decibles relative to full scale
- Short-Time Fourier Transform (STFT) processes overlapping windows of speech
Spectrogram Creation and Interpretation
- In Python,
torchaudioandlibrosaare used to plot spectrograms - Spectrograms use the Fourier Transform
MFCCs
- Mel-Frequency Cepstral Coefficients (MFCC) extract features from audio frames using Fourier Transform, Mel filterbank, Log, and Discrete cosine transform.
- The first 13 MFCC coefficients are commonly used as features.
- Delta and delta-delta derivatives create a 39-dimensional feature vector
Challenges in Speech Recognition
- Speech varies in duration and spectral characteristics
- Phonetic units have variable lengths and overlap in speech
Buckeye Corpus
- Buckeye Corpus is a manually transcribed speech corpus
- It aligns conversational speech and offers phonetic labels located at buckeyecorpus.osu.edu
Word Modeling
- Phone sequence classification is done frame by frame
- Frames are merged if consecutive and have common labels
- Having a few errors may lead to invalid words
Isolated Word Recognition
- Isolated word recognition classifies whole word segments
- This is suitable for small vocabularies (e.g., digits)
Edit Distance
- Edit distance measures the difference between two words
- In this context, a character mismatch is counted
- Edit operations of insertion, deletion, and substitution are needed to determine a final score between words
- Minimum edit distance is the fewest operations to convert one string to another
Dynamic Programming
- Dynamic programming efficiently solves minimum edit distance
- It re-uses prior calculations with a 2-D structure
- A search trellis visualizes possible transitions
- Each transition has a cost
- Edits which are considered are insertion, deletion, correct, and substitution
Dynamic Time Warping (DTW) for Speech
- DTW matches two speech segments using dynamic programming
- DTW: Allows multiple input frames to match a template frame
- DTW: Accounts for slower or faster speaking rates
- DTW: Transitions move to the right in a search trellis using vector distance metrics (e.g., Euclidean distance)
- Cases include: Input > Template, Input = Template, and Input < Template
- The lowest cost path can be found using vector sequences
- Apply Normalization
Normalization in DTW and Speech Recognition
- Divide edit distance by the number of input units to normalize values.
- The Characters are from the minimum edit distance.
- The Frames are from DTW
- Templates are assigned based on the smallest DTW value
- Compute DTW with each template individually and take the minimum
- The templates may not cover all possible variations
DTW with Multiple Templates for Improved Generalization
- The main Idea: Take the average of the templates
- To average templates, align variable lengths
- To align variable lengths, align DTW templates
- Solution: Pick a “master template” and align others to it with DTW, then average vectors
DTW with Average Template
- To implement DTW you can average the features after knowing phone segmentation
- During inference, phone segmentation becomes easier
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