Speech Models Staff Engineering

Formulate a prediction problem in terms of inputs, targets, parameters, and predictions.

Create training, validation, and test splits without data leakage.

Calculate mean-squared error and cross-entropy loss from model predictions.

Perform one gradient-descent parameter update from a given loss gradient.

Train and evaluate a linear regression baseline.

Train and evaluate a logistic classification baseline.

Diagnose underfitting and overfitting from learning curves.

Select evaluation metrics that match regression, balanced classification, or imbalanced classification tasks.

Manipulate PyTorch tensors while controlling shape, data type, and device placement.

Use automatic differentiation to calculate gradients for a simple computation graph.

Explain how activation functions allow a neural network to model nonlinear relationships.

Implement a multilayer perceptron with PyTorch modules.

Build a minibatch training and validation loop.

Compare SGD and Adam using validation behavior.

Apply dropout or weight decay to reduce overfitting.

Save and reload a reproducible model checkpoint.

Interpret a speech waveform in terms of amplitude, time, sample rate, and channels.

Explain how sampling rate and aliasing affect captured speech.

Normalize PCM audio without introducing clipping.

Divide a waveform into overlapping frames and apply a window function.

Interpret an FFT magnitude spectrum for a speech frame.

Compute and visualize a short-time Fourier transform.

Convert a power spectrogram into a log-mel spectrogram.

Identify silence, clipping, noise, and channel problems from waveform and spectrogram views.

Apply one-dimensional convolution to extract local patterns from speech features.

Pad variable-length speech examples and construct valid attention masks.

Trace how an RNN updates its hidden state across a short sequence.

Compute scaled dot-product attention for a small example.

Explain the roles of self-attention, feed-forward layers, residual connections, and normalization in a Transformer block.

Add positional information to an order-independent attention representation.

Compare character, subword, and phoneme tokenizations for speech tasks.

Explain how self-supervised speech pretraining reduces labeled-data requirements.

Assess a speech dataset’s license, consent, privacy, language, and demographic metadata.

Create a manifest linking audio files, transcripts, speakers, languages, and durations.

Normalize transcript punctuation, numerals, casing, and Unicode consistently.

Standardize sample rate, channel layout, amplitude, and file format across a corpus.

Segment long recordings using silence or timestamp boundaries.

Detect common audio–transcript mismatches through targeted data inspection.

Apply speed, noise, or room-response augmentation without changing labels incorrectly.

Construct speaker-aware and language-aware dataset splits that prevent leakage.

Explain why speech frames and output tokens require a flexible alignment method.

Trace the role of blanks and repeated labels in a simple CTC alignment.

Calculate CTC loss conceptually by summing valid alignment paths for a tiny example.

Decode frame-level CTC predictions with greedy decoding.

Apply CTC beam search to retain multiple candidate transcriptions.

Compute word error rate and character error rate from reference and hypothesis text.

Configure a small fine-tuning run for a pretrained CTC speech encoder.

Categorize ASR errors as substitutions, insertions, deletions, normalization errors, or data-quality failures.

Explain the training and inference objectives of encoder–decoder ASR.

Prepare audio, target tokens, and decoder prompts for a pretrained sequence-to-sequence speech model.

Configure a small multilingual fine-tuning run with frozen or parameter-efficient components.

Normalize and evaluate transcripts across Indian scripts using Unicode-aware processing.

Design a test slice for code-switched or mixed-language speech.

Control decoding with language hints, beam width, and repetition constraints.

Apply overlapping chunking and hypothesis stitching to long or streaming audio.

Choose between CTC and encoder–decoder ASR for a stated latency, accuracy, and language scenario.

Normalize raw text into a speakable representation for a TTS pipeline.

Compare grapheme-based and phoneme-based inputs for pronunciation control.

Explain the separate roles of text encoder, duration or alignment model, acoustic model, and vocoder.

Interpret a predicted mel spectrogram as an intermediate speech representation.

Generate audio from a mel spectrogram with a pretrained neural vocoder.

Run controlled inference with a pretrained TTS model.

Evaluate TTS output for intelligibility, naturalness, pronunciation, and speaker similarity.

Diagnose a TTS sample for alignment, prosody, pronunciation, or vocoder artifacts.

Measure latency, throughput, memory use, and real-time factor for speech inference.

Profile preprocessing, model execution, decoding, and postprocessing separately.

Apply batching or dynamic batching while respecting latency constraints.

Use mixed precision or quantization and measure its accuracy–efficiency trade-off.

Export a compatible speech model to an optimized inference format.

Expose batch speech inference through a validated HTTP API.

Design a streaming API that handles partial hypotheses, timeouts, and backpressure.

Load-test a speech endpoint and identify its first scaling bottleneck.

Version speech data, model weights, code, and configuration as one reproducible experiment.

Track training runs with comparable metrics, artifacts, and environment information.

Write data-validation tests for malformed audio, invalid transcripts, and distribution changes.

Write inference tests for output schema, determinism bounds, and latency limits.

Define production monitors for traffic, latency, failures, confidence, and sampled quality.

Design a canary rollout with explicit promotion and rollback criteria.

Apply privacy controls to speech logs, transcripts, and retained audio.

Write an incident postmortem that separates trigger, contributing factors, impact, and prevention.

Translate a speech-product goal into model metrics, service-level objectives, and operational constraints.

Estimate compute capacity and cost from traffic, audio duration, and measured real-time factor.

Create an end-to-end architecture for a multilingual speech service with clear component boundaries.

Write an architecture decision record comparing build, fine-tune, and managed-service options.

Construct a phased technical roadmap with dependencies, risks, and validation milestones.

Review a speech-system proposal for data, modeling, serving, reliability, privacy, and cost risks.

Define a technical mentoring or review process that raises quality without becoming a delivery bottleneck.

Produce a portfolio case study that connects engineering decisions to measurable model and system results.