The good times rolled with the innovation in technology and the upbringing of Artificial Intelligence. How effective and powerful has it become? We all know the answer to this question. There is not a minute that passes by when AI is left behind. Its incredibly branched tools have made our personal and professional lives so much easier.
Do you remember the last time you talked to Google? I do. It's so mesmerizing the way it just listens to me! I converse with Google and Alexa more than with my human friends. The only two buddies I have are actually the virtual assistants. They recognize my voice whenever I feel alone. So, how does is it done? How do they recognize my voice so easily? Let us solve this mystery right now!
Here, we shall understand how speech recognition really works. With an automatic speech recognition system, the motive is to input any continuous audio speech and output the text equivalent. AI poses some of the problems. Automatic speech recognition gets implemented by gathering a large pool of labeled data, training a model on that data, and then deploying the trained model. This must result in accurately labeling new data. Several variables possess a challenge in Speech Dataset Collection and its implementation.
GTS identifies the specific challenges we face when decoding spoken words and sentences into text.
Let me tell you something interesting! Can you imagine a Virtual assistant understanding your emotion? AI model can predict the emotion of the speaker. But how? Just by analyzing the recorded audio clip. The Speech recognition System knows it all. It can understand the variability of the pitch, volume, and speed, ambiguity due to word boundaries, spelling, and context.
Whenever we speak, then vibrations come out in the air. These vibrations are called sinusoidal vibrations. If we have a high pitch, these pitches vibrate faster with a higher frequency than lower pitches. These vibrations are detected by a microphone. They are further transduced from acoustical energy carried in the sound wave to electrical energy. These are recorded audio signals.
The amplitudes in audio signals tell us how much acoustical energy is in the sound, how loud it is. Our voice fluctuates with frequencies at different points in time. So, what an actual signal does? It accumulates all those frequencies.
Have you ever noticed that the way we write a language and the way we speak a language vary greatly? The way we talk to a person on text is completely different than a vocal conversation. Why is it so? There are hesitations, repetitions, fragments of sentences, slips of the tongue, a human listener can filter this out. But, do you think this is easy for a computer to identify just by learning language from audiobooks and newspapers read aloud? It's not! That's why the FFT algorithm, or Fast Fourier Transform, is widely available for this task.
AI model predicts the emotion of the speaker. Let us go through its guide:
We need vast audio data of human voices with labeled emotions. Let us explore a speech emotion recognition database on the Kaggle. Datasets are a mix of audio data (.wav files) from four famous speech emotion databases as Crema, Ravdess, Savee, and Tess. Each audio file in the dataset is attached with just one emotion. It is easy to find emotion as the label is a component in the file name. Therefore, the very first step is to extract all the emotion labels of corresponding audio files from their file names.
Now, we are ready with our audio files and labels. As we know, AI models don't understand anything other than numbers. So, the thing is: How to convert an audio file into numerical representation? The answer is signal processing. Extracting the significant features from this waveform that can help distinguish emotions embedded is a difficult task. A raw audio waveform using signal processing such as zero-crossing rate, spectral centroid, and zooming in are some of the ways to extract features. The variations in the amplitudes and frequencies contained in it can provide various insights.
Now, we have to explore the underlying emotions of our data. To have a balanced AI Training Dataset, we are going to only focus on the top six emotion classes. These classes are anger, disgust, fear, happiness, sadness, and neutral. Beyond these six classes, there come two more emotional classes of surprise and calmness.
Here, is the last step. We need to build the deep learning model. This takes the spectrogram features of an audio file as input and predicts the emotion embedded in it. GTS starts with creating a new deep learning analysis with the emotion’s column as the target column. We split our datasets into 90% as the training set and 10% as the validation set. This helps in choosing our spectrogram vector as input to the model. After processing the quality of AI speech Datasets, the final model is made.
GTS follows the above-mentioned simple steps in order to collect the best quality data to build your Virtual Assistants. AI with the human touch can create wonders. We collect high-quality speech data to train and validate our computer audio models.
We provide all the speech data you need to handle projects relating to NLP corpus, truth data collection, semantic analysis, and transcription. We can help tailor your technology to suit any region or locality in the world, we have a vast collection of data and a robust team of experts. No matter how specific or unique your request for voice data is we can satisfy it. Try us now and enjoy forever!