Subject: [AUDITORY] Visualizing Auditory Network Embedding of English Words From: Malcolm Slaney <000001757ffb5fe1-dmarc-request@xxxxxxxx> Date: Sun, 4 Oct 2026 19:15:57 -0700--Apple-Mail=_8A597BC2-189C-4E97-8CBB-E90947435556 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 Why are some words easier to recognize than others? Some talk about = perceptual neighborhood density, a measure of how many words sound = similar to a query word. Visualizing these distances is the first step. = Thus... Check out the acoustic network embedding (ANE) visualization at https://quicksin.stanford.edu/ane Enter an English word in the search bar and see the words that are = closest to your chosen word. You can change the number of neighbors = displayed and the underling embedding dimension. Move your mouse over a = word to see it=E2=80=99s distance from the search word. This visualization is based on Woojay Jeon's (@xxxxxxxx) Acoustic Neighbor = Embeddings (ACN) <https://github.com/apple-aiml-research/ml-acn-embed>. = His network learns a high-dimensional embedding that puts each English = word into a point in a multi-dimensional space so that acoustic = distances between words are preserved. For more details see this blog posting: = https://ccrma.stanford.edu/blogs/malcolm/visualizing-word-acoustic-neighbo= rhood-embeddings-ane Have fun! Go to https://quicksin.stanford.edu/ane - Malcolm --Apple-Mail=_8A597BC2-189C-4E97-8CBB-E90947435556 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 <html aria-label=3D"message body"><head><meta http-equiv=3D"content-type" = content=3D"text/html; charset=3Dutf-8"></head><body = style=3D"overflow-wrap: break-word; -webkit-nbsp-mode: space; = line-break: after-white-space;"><meta http-equiv=3D"content-type" = content=3D"text/html; charset=3Dus-ascii"><div style=3D"overflow-wrap: = break-word; -webkit-nbsp-mode: space; line-break: = after-white-space;">Why are some words easier to recognize than others? = Some talk about perceptual neighborhood density, a measure of how many = words sound similar to a query word. Visualizing these distances is the = first step. Thus...<div><br></div><div>Check out the acoustic network = embedding (ANE) visualization at</div><div><span class=3D"Apple-tab-span" = style=3D"white-space:pre"> = </span>https://quicksin.stanford.edu/ane</div><div><br></div><div><span = style=3D"font-family: Arial, Verdana, sans-serif; = font-variant-ligatures: normal; background-color: rgb(255, 255, = 255);">Enter an English word in the search bar and see the words that = are closest to your chosen word. You can change the number of = neighbors displayed and the underling embedding dimension. Move your = mouse over a word to see it=E2=80=99s distance from the search = word.</span></div><div><span style=3D"font-family: Arial, Verdana, = sans-serif; font-variant-ligatures: normal; background-color: rgb(255, = 255, 255);"><br></span></div><div><span style=3D"font-family: Arial, = Verdana, sans-serif; font-variant-ligatures: normal; background-color: = rgb(255, 255, 255);">This visualization is based on Woojay Jeon's = (@xxxxxxxx)</span><span style=3D"font-family: Arial, Verdana, sans-serif; = font-variant-ligatures: normal; background-color: rgb(255, 255, = 255);"> </span><a = href=3D"https://github.com/apple-aiml-research/ml-acn-embed" = _fcksavedurl=3D"https://github.com/apple-aiml-research/ml-acn-embed" = style=3D"font-family: Arial, Verdana, sans-serif; = font-variant-ligatures: normal;">Acoustic Neighbor Embeddings (ACN)</a>. = His network learns a high-dimensional embedding that puts each English = word into a point in a multi-dimensional space so that acoustic = distances between words are preserved.</div><div><br></div><div>For more = details see this blog = posting: https://ccrma.stanford.edu/blogs/malcolm/visualizing-word-ac= oustic-neighborhood-embeddings-ane</div><div><br></div><div>Have fun! = Go to = https://quicksin.stanford.edu/ane</div><div><br></div><div>- = Malcolm</div><div><br></div></div></body></html>= --Apple-Mail=_8A597BC2-189C-4E97-8CBB-E90947435556--