Attention-based encoder decoder models remain a popular choice for state-of-the-art automatic speech recognition (ASR). These models combine a powerful audio encoder that extracts rich acoustic features with a decoder that autoregressively produces the ASR output. The decoder handles two critical tasks: (1) building rich text-only context and (2) merging acoustic information from the encoder to ensure the predictions remain faithful to the audio. We observe a systematic pattern across the attention distributions of decoder layers in prior architectures: the initial layers direct most attention towards building textual context, while the later layers largely focus on merging acoustic and textual information for the final predictions. Leveraging this key insight, we propose **BlockDecoder**, a novel decoder architecture comprising two distinct components: a text encoder that is purely text-based, and a **Merger** that combines information from the audio encoder and text encoder to generate output tokens. Unlike traditional decoders, the **Merger** autoregressively predicts a sequence of K tokens within a *block* of size K, while relying on the same precomputed contextual information from both text and audio encoders across the block. This design choice allows for the efficient reuse of encoder representations. The separation of the decoder into the text encoder and the **Merger** promotes modularity and more flexible control of parameters via the number of text encoder and **Merger** layers. As a result, **BlockDecoder** yields a significant speedup ($\sim2$x) compared to traditional decoders, across diverse datasets, languages, and speech tasks, without any degradation in performance. The code is available at https://github.com/csalt-research/blockdecoder.

Speech accents pose a significant challenge to state-of-the-art automatic speech recognition (ASR) systems.Degradation in performance across underrepresented accents is a severe deterrent to the inclusive adoption of ASR.In this work, we propose a novel accent adaptation approach for end-to-end ASR systems using cross-attention with a trainable set of codebooks.These learnable codebooks capture accent-specific information and are integrated within the ASR encoder layers.The model is trained on accented English speech, while the test data also contained accents which were not seen during training.On the Mozilla Common Voice multi-accented dataset, we show that our proposed approach yields significant performance gains not only on the seen English accents (up to 37% relative improvement in word error rate) but also on the unseen accents (up to 5% relative improvement in WER).Further, we illustrate benefits for a zero-shot transfer setup on the L2Artic dataset.We also compare the performance with other approaches based on accent adversarial training. Fbank Feats CTC