17 February 2025

Imagine a world where machines not only hear your words but understand them with astonishing precision. This astonishment is made possible by the ingenious design of Speech Recognition Acoustic Models. Welcome to the wondrous world of Speech Recognition Acoustic Models, where speech becomes a universal language comprehended by silicon minds. The futuristic vision is made possible by the intricate workings of speech recognition acoustic models, the unsung heroes behind the scenes of voice assistants, transcription services, and a myriad of other applications. Speech recognition acoustic models are a vital component of automatic speech recognition (ASR) systems. They play a key role in converting spoken language into written text. Now let us deep dive into the concept pf this wonderful topic. What is Speech Recognition – Acoustic model? Automatic Speech Recognition (ASR) is a technology that enables machines to convert spoken language into written text. This technology has numerous applications including voice assistants (like Siri, Alexa), transcription services, customer service applications, and more. It’s essentially a mathematical model that learns to understand the relationship between audio input (speech signals) and the corresponding linguistic units, such as phonemes or sub-word units. Speech recognition acoustic models have evolved significantly with the advent of deep learning. Models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are widely used in acoustic modelling. These models are at the core of technologies like voice assistants (e.g., Siri, Alexa), transcription services, and more. They play a crucial role in enabling machines to understand and interact with spoken language. Some interesting insights of this model: In the following attachments, you can observe that the given model has been searched for an average of 60 times every month for the past 2 years. India and USA are the 2 countries mostly dealing with this model. How Speech Recognition Acoustic model works? The following steps are followed: 1.Audio Input: 2. Feature Extraction: 3. Deep Learning Architecture: 4. Learning Relationships: 5. Probabilistic Output: 6. Integration with Language Model and Lexicon: 7. Decoding: 8. Final Transcription: 9. Adaptability and Fine-tuning: 10. Feedback Loop: Overall, a speech recognition acoustic model plays a crucial role in understanding the acoustic characteristics of speech and converting it into a form that can be processed by a computer algorithm to generate accurate transcriptions. Speech Recognition model code: Now we are going to see codes with the use of popular ASR libraries or APIs Google Speech-to-Text API: we will be working with codes with the use of Python Library SpeechRecognition,which wraps various ASR(Acoustic Speech Recognition) engines, including Google Web Speech API, Sphinx, etc. In the above code, SpeechRecognition library is installed. The module speech_recognition is imported with alias sr. The Recognizer class is part of the SpeechRecognition library in Python. This class is used to perform various operations related to speech recognition, such as listening for audio input from a microphone, recognizing speech from an audio source, and working with different speech recognition engines. Method record is used to record audio from the source. recognize_goggle, this method recognizes speech using the Google Web Speech API.If audio is not understandable error will raise. Google Web Speech API, also known as the Web Speech API, is a browser-based API that allows web developers to integrate speech recognition capabilities into their web applications. It enables applications to convert spoken language into text, making it useful for tasks like voice commands, transcription services, and more. Hugging Face’s transformers Library: ‘Transformers’ library is used here to create pipeline where task and model must be taken and thus ASR is performed on audio file. This is the output we got and the text from the audio is extracted. Language Model in Speech Recognition: In speech recognition, a language model plays a crucial role in improving the accuracy and fluency of the recognized text. It provides additional context to help the system make more accurate predictions about the next word or sequence of words in the transcription. Important properties of language model in speech recognition: The language model can be built using various approaches: By combining an acoustic model with an appropriate language model, you can significantly improve the accuracy and fluency of transcriptions in a speech recognition system. Sample code to understand the language model for speech recognition: We have imported required deep learning libraries and then under the variable corpus we have given a text. Tokenization to be done where we use the Tokenizer from Keras to convert text into sequences of integers. Then Input Sequences and Labels is created where we create sequences of increasing length from the tokenized text. These sequences are used as input, and the next word in each sequence is used as the label. Model is Defined where we define a simple LSTM-based neural network. Model Compilation is done where we compile the model with suitable loss function and optimizer.Model is trained where we train the model on the input sequences and labels.Text Generation: We use the trained model to generate text. Starting with a seed text, we predict the next word and append it to the seed text. Model is trained with 100 epochs and we got good accuracy of 93.75% with loss of 0.7415. As we can see we have got next 5 predicted words as ‘icc cricket world cup trophy’. Acoustic wave simulation Acoustic wave simulation is a technique used in the development and testing of speech recognition systems. Let us see how acoustic wave simulation is relevant to speech recognition: 1. Data Augmentation: 2. Noise and Environmental Variation: Simulating different acoustic environments (like noisy environments, reverberant rooms, etc.) helps in training the model to be more robust to different real-world scenarios. 3. Speaker Variability: Simulation can be used to introduce variations in speaker characteristics, like pitch, accent, or gender. This helps the model generalize better across different speakers. 4. Adversarial Attack Simulation: In security-related scenarios, simulating adversarial attacks helps in making speech recognition systems more secure against intentional distortions. 5. Testing and Evaluation: Simulated data is often used in testing and evaluating

Welcome to the fascinating world of generative models in recommender systems, where the power of artificial intelligence meets the art of personalization! Generative models for recommendation systems – an innovative approach that surpasses traditional algorithms to craft personalized suggestions like a master artist painting vibrant portraits of your interests Imagine a recommender system that not only understands your preferences but has the creative power to implore entirely new possibilities adapted just for you. In this domain, data is not just analysed; it becomes a canvas on which these models paint a rich tapestry of novel and diverse recommendations, promising a journey of delightful discoveries at every turn. Let us begin on this fascinating exploration of generative models, where the boundaries of personalized recommendations are redefined, and the art of suggestion reaches unprecedented heights. Let us dive deep into these concepts in this blog. Read the entire blog to gain immense knowledge on this topic. What is Generative model for Recommendation system? Generative models for recommender systems are a class of machine learning models that aim to understand the underlying patterns in user-item interaction data and generate personalized recommendations. This means that generative models not only predict user preferences for existing items but also have the ability to generate entirely new item recommendations that are tailored to each user’s unique preferences. The fundamental idea behind generative models is to learn the patterns and relationships within the user-item interaction data in a way that allows the model to create new, realistic examples. Let us check some facts about Generative models: This is searching trends of Generative Model in India for the past 30 days. Daily average search of this topic is more than 75 times. This is how the world is proceeding with the concept of Generative Model. Let us check the states where several companies are working with this concept. As we can see Karnataka and Telangana are the states leading from front. These are the countries searched the most of this topic in the last 30 days. China, Singapore leading the search history. We have 3 popular generative model: Variational Autoencoder (VAE) and Generative Adversarial Network (GAN), Autoregressive model, these can grasp the intricate relationships between users and items. They create a multidimensional representation of preferences, allowing for a better understanding of individual tastes and preferences. Let us discuss VAE and GAN in detail. Variational Autoencoder (VAE): It is a generative AI algorithm that uses deep learning to detect anomalies, remove noise and generate new content. It is used to learn a probabilistic mapping of input data into a lower-dimensional latent space, and from that latent space, generate new data that resembles the original input. The basic structure of a Variational Autoencoder consists of two main components: 1.Encoder: The encoder takes the input data (e.g., user-item interactions in the case of recommender systems) and maps it into a latent space that is lower dimension space. The output of the encoder is not a deterministic encoding, but rather a probability distribution of the latent variables. Now Question arises PCA and Encoder same? No, as PCA learns linear relationship and Encoder learns Non-Linear relationship. From the figure we can see that PCA is learning the data having linear structure and Encoder is learning the data having complex structure. Once Encoder uses linear activation functions then in that case PCA=Encoder. 2.Decoder: The decoder takes a sample from the latent space (i.e., latent variables) and reconstructs the original input data from that sample. Like the encoder, the decoder also produces a probability distribution for the output data. Let us now see what VAE for Recommendation system is: Here is a high-level overview of how a VAE-based recommender system works: 1.Data Representation: Convert user-item interactions into a matrix format, where rows represent users, columns represent items, and the entries contain user-item interactions (e.g., ratings, binary indicators, or other relevant metrics). 2.VAE Architecture: 3.VAE Training: 4.Recommendation Generation: Let us check one example code of Variational Encoder. We are considering Movies Lens dataset and working with it to develop the Recommendation model. First, we install required packages. ! pip install tensorflow –upgrade import numpy as np import pandas as pd from keras.layers import Input, Dense, Lambda from keras.models import Model from keras import backend as K from keras import objectives from sklearn.model_selection import train_test_split   Load data and do preprocessing. data = pd.read_csv(‘/content/ratings.csv’) # Preprocess the data to create the user-item interactions matrix   user_item_matrix = data.pivot(index=’userId’, columns=’movieId’, values=’rating’) user_item_matrix = user_item_matrix.fillna(0)   # Convert the user-item interactions matrix to a NumPy array   user_item_matrix_array = user_item_matrix.to_numpy()   # Spliting the data into train and test   train_data, test_data = train_test_split(user_item_matrix_array, test_size=0.2, random_state=42)   Defining parameters, Building Encoder, Decoder and combining encoder and decoder to build the model. # Define VAE parameters original_dim = user_item_matrix_array.shape[1]  # Number of items (features) latent_dim = 32                                 # Number of latent dimensions batch_size = 64 epochs = 50 # VAE encoder architecture x = Input(shape=(original_dim,)) h = Dense(64, activation=’relu’)(x) z_mean = Dense(latent_dim)(h) z_log_var = Dense(latent_dim)(h) # Reparameterization trick def sampling(args):     z_mean, z_log_var = args     epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=1.0)     return z_mean + K.exp(z_log_var) * epsilon z = Lambda(sampling)([z_mean, z_log_var]) # VAE decoder architecture decoder_h = Dense(64, activation=’relu’) decoder_mean = Dense(original_dim, activation=’sigmoid’) h_decoded = decoder_h(z) x_decoded_mean = decoder_mean(h_decoded) # Define VAE custom loss function (binary cross-entropy + KL divergence) def vae_loss(x, x_decoded_mean):     xent_loss = objectives.binary_crossentropy(x, x_decoded_mean)     kl_losss = – 0.5 * K.sum(1 + zlog_var – K.square(z_mean) – K.exp(zlog_var), axis=-1)     return xent_loss + kl_losss # Compile VAE model vae = Model (x, x_decoded_mean) vae.compile(optimizer=’adam’, loss=vae_loss) Training the model. # Train the VAE model vae.fit(train_data, train_data,         shuffle=True,         epochs=epochs,         batch_size=batch_size,         validation_data=(test_data, test_data)) # Use the trained VAE model to generate recommendations for a user def generate_recommendations(user_data, vae_model):